Federated learning (FL) is a transformative paradigm for the Internet of Things (IoT), enabling decentralized model training across distributed IoT devices while reducing reliance on centralized data collection. Crucially, FL cuts communication overhead, an essential benefit in bandwidth-limited IoT environments. However, repeated gradient exchanges between edge clients (e.g., sensors, mobile devices) and the central server expose vulnerabilities to gradient leakage attacks (GLAs), allowing adversaries to reconstruct private training data from shared gradients. While various gradient protection strategies, such as differential privacy, sparsification, and clipping, have been introduced to mitigate this risk, most existing GLAs are designed for specific protection schemes and fail under heterogeneous deployments. In this work, we propose a unified GLA framework that targets diverse gradient protection techniques in FL systems. Our approach tackles two core challenges: (i) aligning protected gradients with their raw counterparts to enable robust feature extraction, and (ii) identifying critical features for efficient and accurate data reconstruction.We introduce a Taylor-based gradient approximation method for alignment and design a feature reconstructor that enhances both performance and computational efficiency. Extensive experiments across various FL scenarios demonstrate the framework’s superior reconstruction capability under different protection schemes, emphasizing the need for robust privacy-preserving mechanisms in IoT networks.

Software-defined Networking (SDN) is an emerging network architecture. The decoupled data and control plane provide flexible manageability and programmability to the network. One of the core components in SDN switches to direct traffic forwarding is the flow table, which is usually stored in the ternary content addressable memory (TCAM) with limited space and high power consumption, making the flow table a potential target for attacks. This paper examines the Low-Rate Flow Table Overflow (LRFTO) attacks, which fill the flow table and render it unavailable by continuously sending attack rules to occupy the space. We propose a quantitative model of LRFTO attacks to describe the attack behavior, analyze its difference from legitimate traffic, and summarize some key aspects and features to distinguish attack rules from legitimate rules. We also propose LRFTO-ADMS, a system that utilizes machine learning-based classification as its core to evict suspicious rules, thereby ensuring flow table availability. Experimental results show that the threat model can adapt correctly to the network environment. The proposed LRFTO-ADMS can evict attack rules with an accuracy of more than 95% and low overhead while protecting the flow table and the legitimate rules from being unavailable.

DNS recursive resolver plays the role of intermediary agent in domain name query service, making it easy to observe various DNS flooding attack behaviors. The programmable data plane has promoted innovation in in-band attack detection, but previous work has not concentrated on DNS services, and there are concerns with mistakenly killing benign traffic and low classification accuracy. In this paper, we propose DNSGreen, a comprehensive defense system against bounce-style DNS DDoS attacks in the programmable data plane. DNSGreen discusses flow and packet characteristics under anomalous behavior, employing a “flow-detection and packet-filtering” pattern to ensure effective defense while allowing benign traffic to pass through as much as possible. Furthermore, DNSGreen designs a whitelist collection module, which eliminates the need to inspect trusted streams in subsequent filtering and thereby reducing the occurrence of false positives. Finally, DNSGreen improves the allocation method of the statistical structure to enhance the accuracy of system alarms. We deploy DNSGreen on BMv2 switches and conduct tests in three attack scenarios, demonstrating that DNSGreen safeguards normal users with excellentF0:5score while filtering attack traffic.

Mobile devices have penetrated our daily life, and so have the fast chargers. However, while improving users' charging efficiency, fast chargers may pose severe security threats. In this work, we present a novel side-channel attack called ChargerWhisper, which exploits the acoustic signals generated by fast chargers to infer users' private information in an on-charging mobile device. In particular, when users perform different activities on the charging device, the fast charger will supply distinct amounts of power outputs to the mobile device. By conducting a deep investigation of the fast charger circuits, we find that the electronic components, i.e., inductors, capacitors, and transformers, vibrate at certain frequencies and generate high-frequency inaudible sounds. Meanwhile, the leaked sound frequencies are highly correlated with electric intensity flowing through these electronic components (charger's output power). To demonstrate the acoustic side-channel attack leveraging fast chargers, we present two specific exploitation scenarios: a website fingerprinting attack to identify users' on-browsing websites, and a PIN inference attack to infers the device's unlock PIN. Extensive evaluations are performed on off-the-shelf devices. The results show that both attacks are robust under diverse usage settings and achieve good performance in inferring users' privacy information. These findings highlight the essential need to address unnoticed privacy leakage risks associated with fast chargers.

Face recognition (FR) brings convenience to peoples lives while also posing security risks. Some malicious users employ FR attacks to impersonate the identity of a target. To reveal the security risks, recent work has attacked black-box FR models by utilizing substitute models to generate adversarial face images that are misclassified as the target individual due to the attack transferability of substitute models. However, the substitute models cannot accurately approximate the target model that leads to a decrease in FR attack success rate and adversarial face image quality. To address the issue, we propose the PPOM-Attack, a substitute model-free Perturbation Prediction and Optimization Method for black-box adversarial Attack against face recognition. PPOM-Attack directly obtains feedback from the target model instead of using substitute models, it avoids any discrepancy with the attack objective. To achieve this goal, we design a proximal policy optimization (PPO)-based agent to predict the perturbation regions in the face image and self-adaptively disturb the regions. To maintain high-quality adversarial face images, we further propose a minimum brightness offsets method specifically designed to generate perturbations that minimize the feature embedding difference between the adversarial and targeted face images. The experimental results show that our approach outperforms state-of-the-art FR attack methods by an average of 21.7% in terms of attack success rate, while achieving better image quality on seven FR models.

Efficient operation of integrated energy system (IES) is hampered by multicoupled uncertainties across the source-load-price. Existing studies often model isolated or partial uncertainties, lacking full-dimensional, systematic characterization. Model predictive control (MPC) and deep reinforcement learning (DRL) exhibit significant complementary potential, and their combination holds promise for revealing deep interconnections among multiuncertainties. This article is the first to explore the collaborative application of MPC and DRL in the IES field and proposes a novel TD3MPC framework. In this framework, MPC is embedded as an expert actor within the twin delayed deep deterministic policy gradient (TD3), providing safe, physics-compliant baseline actions. TD3 then employs data-driven exploration around this baseline to compensate for model mismatch and improve multiuncertainty responsiveness. To balance the learning and exploration of the intelligent agent, a hybrid prioritized experience replay mechanism is designed. Additionally, the conventional single-timestep state is extended to an augmented state space incorporating historical and future information, with state transitions modeled via probabilistic scenario techniques to reinforce TD3’s generalization ability for complex energy problems. Simulation results demonstrate that the proposed strategy exhibits strong robustness and reduces economic costs under multiuncertainty environments.

Software-Defined Network (SDN) revolutionizes traditional network structures by isolating the data plane and the control plane, which offers greater flexibility in managing network resources. Nevertheless, SDN remains vulnerable to certain threats inherited from the traditional network, including Distributed Low-rate Denial-of-Service (DLDoS) attack. This attack is more subtle and harder to detect than traditional Distributed Denial-of-Service (DDoS) attacks, because it employs a lower average attack rate. We design a real-time detection and mitigation system named DOE-DTL specifical for the DLDoS attack in SDN. For the DLDoS attack detection, we utilize Machine-Learning (ML) methods to construct a detection model and introduce it in DOE-DTL. In the construction, we leverage Extreme Learning Machine (ELM) and make a dual optimization using Whale Optimization Algorithm (WOA). For the DLDoS attack mitigation, we use double thresholds to determine the attack sources and make corresponding mitigation rules. DOE-DTL innovatively combines the Programmable Data Plane (PDP) in detection and mitigation, shifting some control plane tasks to the data plane. Performance assessments reveal that DOE-DTL ensures fast, accurate attack identification and low-latency mitigation while maintaining low resource usage.

Many real-world relationships can be effectively represented as edge-labeled graphs, where edge labels encode semantic information vital for graph computations. Analyzing communities within such graphs is of great importance, with cohesive subgraph queries being a fundamental problem in graph analysis. Among these, the k-core model is one of the most widely studied frameworks for cohesive subgraph queries and has attracted significant attention over the past decade. However, most existing k-core models disregard edge labels, limiting their applicability to semantic-aware analyses. In this paper, we propose an index-based method to address the problem of querying k-cores with label constraints in edge-labeled graphs. We first introduce a basic index that maintains core decomposition results for each possible label set. Then, to further optimize performance, we propose an advanced index structure that captures the label containment properties of k- cores by computing canonical label sets for each possible$k$and each vertex. This approach can greatly reduce the index size while ensuring efficient query processing. We also design an optimized algorithm for constructing our index, achieving a significantly faster runtime than naive construction methods. Extensive experiments on real graphs demonstrate the efficiency and effectiveness of our index-based algorithms.

Bipartite graphs are ubiquitous, such as E-commerce network and gene networks. Efficient analysis of (p, q)- biclique is one of the important problems over bipartite graphs. However, existing works over (p, q)-biclique suffer from two main challenges. Firstly, most of them only focus on static graphs, while lots of bipartite graph-structured data are constantly created in real world, forming streaming bipartite graphs. Secondly, results of (p, q)-biclique could be of exponential scale, which may overwhelm analysts. Hence, computing top$k$most important (p, q)-bicliques is worth considering. In this paper, we study a new problem to maintain top$k$densest (p, q)-bicliques over a streaming bipartite graph. We propose a new framework, called as TopK-BC, to compute the proposed problem effectively. We design an efficient pruning strategy for edge deletion stage, called IDpruning. In particular, we maintain an intermediate density for each edge to efficiently compute high-density (p, q)-bicliques. Also, we introduce effective optimization technologies to filter out unpromising intermediate results and further enhance the performance. Extensive experiments over real world datasets confirm the efficiency and effectiveness of our solution.

Federated Learning (FL) has emerged as a privacy-preserving framework under which multiple participants jointly solve the a collaborative training and user privacy problem. Recent studies find that private training data can still be leaked by the exchanged gradients based on optimization or analytic, i.e., gradient inversion attacks (GIAs). To enhance privacy, adversarial perturbations (AP) are attempted to be applied in FL by introducing carefully crafted noise into the local gradients. However, the effectiveness of adversarial perturbations in strengthening privacy against GIAs remains underexplored. In this work, we empirically evaluate adversarial perturbations on the resistance of GIAs. We show that even adversarial perturbations added to the gradients can still leak training data. We propose an adversarial perturbation gradient inversion attack, APT. Specifically, we design a feature extraction method to extract data features from adversarial perturbation gradients by utilizing the linear layer. Moreover, we design a feature reconstruction method to reconstruct data by a key feature reconstructor. Extensive experiments demonstrate that our method achieves high-quality gradient inversion from adversarial perturbation gradients, surpassing state-of-the-art methods that commonly fail in more challenge scenarios. Overall, our work explores the defense effectiveness as well as reveals the vulnerability of AP under GIAs. We hope this work provide valuable insights into leveraging adversarial perturbations for privacy defense and inspire future research on robust privacy-preserving mechanisms in FL.

The harm caused by deepfakes is becoming increasingly serious, including financial fraud, guiding political public opinion, and more. The vast majority of deepfake detection methods typically perform well on uncompressed deepfake videos, achieving satisfactory results. However, when facing deepfake videos with different compression rates, the effectiveness of certain detection methods will significantly decrease, and they may even be unable to detect compressed videos. To address this challenge, we introduce a new detection mechanism: landmarks inference mechanism. Based on this, we propose a simple and efficient model LDInfer for inferring landmarks to achieve deepfake video detection. Our method utilizes the inference between facial landmarks and their preceding and succeeding frames to improve detection accuracy, which remains effective even in compressed deepfake videos. This method first infers landmarks and verifies their authenticity using the correlation between landmarks in the previous and subsequent frames. Even if the video is compressed, the correlation between facial landmarks in the preceding and following frames remains high. Comparative experiments show that our method outperforms existing methods at various compression rates.

Password management is a fundamental aspect of security for Internet of Things (IoT) devices. However, despite the availability of established guidelines and best practices, the implementation of password management in IoT firmware often falls short, leading to vulnerabilities and potential breaches. Because of the lack of automated tools, the severity and pervasiveness of broken password management of IoT firmware has been less understood. In this paper, we present FIRMPASS, a new tool to identify broken password management of Linuxbased IoT firmware. Particularly, we establish general password management models for Linux-based IoT devices based on password management processes and related vulnerabilities. To automatically identify the vulnerabilities, FIRMPASS employs a query-driven approach to locate the firmware that violate the properties of correct password management. Specifically, we manually define four rules that should be complied with, encode the rules with queries, and check the queries in the firmware, which leads to the discovery of four types of vulnerabilities. Evaluation of 615 IoT firmware images uncovers 67 vulnerabilities, including 37 previously unknown issues. Our work underscores the urgent need for assessment solutions for IoT firmware.

With the widespread sharing of personal face images on the Internet, systems based on face recognition encounter the real threat of being breached by potential adversaries who are able to access individuals’ face images and use them to intrude the systems. In this article, we propose a novel privacy protection method in the sparsified multiscale feature subspaces to protect sensitive facial features, taking care of the influence or weight-ranked subspace coefficients on the privacy budget, named “ranked differential privacy (RDP).” After the multiscale subspaces’ decomposition, the lightweight Laplacian noise is added to the dimension-reduced sparsified subspaces’ coefficients according to the geometric superposition method. Then, we rigorously prove that the RDP satisfies$\varepsilon _{0}$-differential privacy. After that, the nonlinear Lagrange multiplier method (LM) is formulated for the constraint optimization problem of maximizing the utility of protected face images of high-visualization quality with sanitizing noise, under a given privacy budget$\varepsilon _{0}$. Then, two methods are proposed to solve the nonlinear Lagrangian multiplier method (LM) problem and obtain the optimal noise scale parameters: 1) the analytical normalization approximation (NA) method with identical average noise scale parameter for real-time online applications and 2) the LM optimization method via gradient descent (LMGD) to obtain the nonlinear solution through iterative updating for more accurate offline applications. Experimental results on two real-world datasets show that our proposed RDP outperforms other state-of-the-art methods: at a privacy budget of$\varepsilon _{0} = 0.2$, the peak signal-to-noise ratio (PSNR) of the optimized RDP is about ~10 dB higher than (10 times as high as) the highest PSNR of all state-of-the-art methods compared.

The deep integration of Internet of Things (IoT) and cloud computing promotes a wide deployment of body area networks (BANs) for smart health services. The data security raises new challenges when patients’ health records (HRs) are uploaded into the cloud server by BAN. The attribute-based encryption (ABE) primitive is a potential option to ensure HRs security, which provides the data confidentiality guarantee and fine-grained access control simultaneously via cryptographic means. However, most ABE schemes are unsuitable to be deployed in smart health application as access policies associated with encrypted HRs reveal patient’s privacies. Though the recently proposed ABE with partially hidden access policy based on composite order can alleviate the privacy leakage by only disclosing the attribute names and concealing the practical attribute values, the exposed attribute names still leak individual privacies. In this article, we put forward a privacy-enhanced and efficient ABE construction with fully hidden access policy over prime order group based on the prominent ABE construction due to Bethencourt et al.. Our scheme hides the sensitive attributes in the access structure by several nontrivial designs without compromising the correctness and security. Moreover, our scheme’s performance is far superior to the attribute partially hidden schemes. Extensive experiments demonstrate the conclusion.

Low-rate Denial of Service (DoS) attacks can significantly compromise network availability and are difficult to detect and mitigate due to their stealthy exploitation of flaws in congestion control mechanisms. Software-Defined Networking (SDN) is a revolutionary architecture that decouples network control from packet forwarding, emerging as a promising solution for defending against low-rate DoS attacks. In this paper, we propose Trident, a low-rate DoS attack mitigation scheme based on port and traffic state in SDN. Specifically, we design a multi-step strategy to monitor switch states. First, Trident identifies switches suspected of suffering from low-rate DoS attacks through port state detection. Then, it monitors the traffic state of switches with abnormal port states. Once a switch is identified as suffering from an attack, Trident analyzes the flow information to pinpoint the malicious flow. Finally, Trident issues rules to the switch's flow table to block the malicious flow, effectively mitigating the attack. We prototype Trident on the Mininet platform and conduct experiments using a real-world topology to evaluate its performance. The experiments show that Trident can accurately and robustly detect low-rate DoS attacks, respond quickly to mitigate them, and maintain low overhead.

The demand for resilient watermarking technology in the context of the screen-shooting scenario is steadily on the rise. The principal objective of this technique is to embed messages into the cover image, with the ability to effectively recover the message from the screen-captured image at the extraction end. However, current watermarking methods result in low visual quality watermarked images and are insufficiently robust in screen-shooting scenarios. This is mainly because they only utilize spatial domain information during embedding, and they do not consider the impact of noise that introduced during screen capturing. This paper introduces an innovative network framework, including the wavelet domain concatenation and recovery mechanism, to overcome the dual challenges encountered in robust watermarking, namely visual fidelity and robustness. For fidelity, we present a cascade network operating in the wavelet domain. This network excel at detecting watermark information in the wavelet domain. This capability makes it more sensitive to high and low-frequency details. Discrete wavelet transform can make CNN focus on different frequency characteristics, and the use of discrete inverse wavelet transform in upsampling can make the information high fidelity. As a result, it can more accurately identify and preserve critical visual details in this frequency domain, leading to an overall enhancement in visual quality. For robustness, a recovery network is specifically designed to mitigate the influence of noise introduced during screen-shooting on watermark information extraction. Experimental validation of our proposed method substantiates its effectiveness in significantly enhancing the visual quality and the accuracy of the watermarked images.

With the rise of telemedicine and intelligent diagnostics, the efficiency and accuracy of healthcare services have been significantly enhanced. However, the highly sensitive nature of medical images makes protecting patient privacy during transmission and storage a critical challenge. In this paper, we propose a secure medical image encryption scheme based on cross-ring Josephus scrambling and two-dimensional cellular automata, designed to safeguard medical images. First, we introduce a novel two-dimensional chaotic map (2D-CICM) with an expanded parameter range to generate high-quality key sequences for encryption. Next, we design a cross-ring Josephus scrambling algorithm for pixel permutation, where the eliminated pixel is determined by both inter-ring and intra-ring step sizes. Following this, we develop a diffusion mechanism based on interaction rules defined by six types of two-neighbor structures within a cellular automaton framework. To enhance key sensitivity and image-specific security, we also incorporate the 512-bit hash value of the plaintext image to dynamically update the initial keys, ensuring that the encryption key sequences are unique for each image. Comprehensive security analyses and performance evaluations confirm that the proposed scheme provides strong encryption performance and effectively resists common attacks, while maintaining computational efficiency suitable for medical applications.

The local deployment of federated large language models (FLLM) has further advanced the development of edge intelligence. However, the resource constraints of end devices, device heterogeneity, and the non-independent and identically distributed (Non-IID) nature of data pose significant challenges to the application of FLLM. To address this issue, we propose an Adaptive Asynchronous Accelerated FLLM (Tri-AFLLM) algorithm to achieve the efficient utilization of limited resources and improve model accuracy in the edge computing (EC) scenarios. Specifically, Tri-AFLLM first ships an off-the-shelf LLM, i.e., CLIP, to each end device, keeping the backbone parameters frozen and updating only the parameters of the adapter containing two linear transformation layers by using momentum gradient descent (MGD). Next, a toy example is provided to illustrate the necessity of using different numbers of local iterations for heterogeneous devices in resource-constrained environments. Subsequently, the convergence bound of the Tri-AFLLM under a given resource budget is discussed. Then, we formulated the bound into a resource consumption minimization problem with the number of local iterations as the optimization variable under a given model accuracy to mitigate the contribution disparity of local models to the global aggregation. Finally, extensive experiments are conducted to validate the superiority of Tri-AFLLM in terms of resource consumption, model accuracy, and addressing the Non-IID problem.

Interpretable deepfake detection is gaining attention for providing explainable, trustworthy results, avoiding the limitations of ‘black-box’ models. Current interpretable methods focus on visible artifacts in low-visual-quality deepfakes, but these artifacts become less apparent in high-visual-quality deepfakes generated by advanced models. With advancements in deep generative models, producing high-visual-quality deepfakes has become a strategy to evade detection. To address this, we propose${\sf ADA-FInfer}$, an adaptive frame selection and interpretable face representation inference method for detecting high-visual-quality deepfakes.${\sf ADA-FInfer}$adaptively selects frames by analyzing optical flow to reveal manipulations. We also introduce an adaptive attack method that manipulates specific frames, and our adaptive selection strategy shows resistance to such attacks.${\sf ADA-FInfer}$uses an encoder to learn face representations from source and target faces, applying a representation-prediction loss to maximize the distinction between real and fake videos. To provide further insights, we employ the joint entropy, mutual information, and conditional entropy analyses to explain the method's effectiveness. Extensive experiments and ablation studies demonstrate that${\sf ADA-FInfer}$achieves promising performance in detecting high-visual-quality deepfakes.

With the development of Wi-Fi sensing, wireless-based gait recognition has become increasingly important as it supports a wide range of applications (person identification, disease diagnosis, etc.). However, two serious challenges limit the universal deployment of such Wi-Fi vision schemes: i) the limited bandwidth of Wi-Fi severely restricts the granularity of gait recognition, and ii) users non-gait behaviors (e.g., stopping and turning) interfere with the extraction of gait-related features. In this paper, we propose CSID, which can achieve robust gait recognition under the limited bandwidth conditions of commercial Wi-Fi devices. Specifically, we use a neural network to generate super-resolution spectrograms of channel state information (CSI), overcoming the limitation of insufficient Wi-Fi bandwidth. To overcome the challenge of non-gait behavior interference, considering the human-incomprehensible nature of Wi-Fi spectrograms, we adopt cross-domain adversarial training and further extract gait features that are independent of the interference behaviors by learning domain-independent representations. We conducted a large number of experiments in different indoor environments, and the average person identification rate of the CSID system reached 91.6%. These results demonstrate that the CSID system is promising and could be used as a complement to visual person identification systems in the future.

Transferring important files, photos, and other sensitive data between laptops and smartphones has become a routine necessity in daily life. Device pairing acts as the most fundamental need to secure the communication channel between two unconnected devices. Traditional pairing methods leveraging PINs or QR codes require tedious human efforts in the pairing procedures to establish a shared communication key. Nevertheless, these designs are vulnerable to shoulder-surfing attacks in which attackers might record and replay the pairing credentials. It is preferred to have more intuitive pairing designs that minimize users’ overhead in the pairing process while providing secure keys for communication purposes. In this paper, we propose MIPair for laptop-phone pairing by leveraging magnetic induction (MI) signals. MIPair is based on a key observation that changes in the CPU workload of a device cause variations in internal current, thereby inducing changes in surrounding magnetic fields. Moreover, the trends of MI signal variations are highly correlated with CPU workload trends. Thus, users simply need to place a smartphone on the keyboard of a laptop. By randomly altering the workload of the laptop through a stimulation program, the smartphone can capture MI signals with similar changing trends, thereby converting them into similar bit sequences that form the basis of a symmetric key. We propose essential techniques to overcome challenges such as time asynchronization between two devices, unfixed state transition time in MI signal, and shared key distribution from the two similar bit sequences. Our real-world experiments demonstrate reliable pairing as well as robustness against common attacks and high randomness of the generated keys. When generating a 128-bit key, MIPair achieves a successful pairing rate as high as 98% and a usable pairing time of 8.35 seconds.

Spatial keyword query has emerged as a critical service in mobile cloud, enabling cloud servers to retrieve spatiotextual objects within a mobile user's query range that contain specified query keywords. Numerous secure spatial keyword query schemes have been developed to enable geometric range queries and keyword searches on encrypted spatial data. However, spatial keyword queries are typically designed for searching high-dimensional spatial data across arbitrary geographic ranges. Most of them fail to handle arbitrary geometric range queries and efficient spatial keyword query over high-dimensional encrypted data. To address these issues, we propose a high-dimEnsional and Privacy-preserving Spatial Keyword Query (EPSKQ) scheme with arbitrary geometric ranges over encrypted spatial data, leveraging Hilbert curve encoding and Enhanced Matrix-based Inner Product Encryption (EMIPE). In EPSKQ, spatial locations and multi-keywords are encoded into compact vectors, and arbitrary geometric range queries are transformed into range intersection tests. To reduce computational overhead, we employ vector bucketing technique to partition large-size vectors into several small-size sub-vectors. Furthermore, we design a novel index structure called Hilbert Binary tree (HB-tree) to optimize range intersection tests. Based on HB-tree, we propose an enhanced spatial keyword query scheme, named EPSKQ+, which further improves query performance. Security analysis demonstrates that both EPSKQ and EPSKQ+ achieve semantic security against indistinguishability under chosen-plaintext attack (INDCPA). Extensive experimental evaluations show that the proposed EPSKQ and EPSKQ+ schemes significantly outperform state-ofthe-art schemes in terms of computational and communication costs, with EPSKQ+ being 9× and 3× faster than the state-ofthe-art schemes in the index build and query phase, respectively

The TCP protocol’s inherent lack of built-in security mechanisms has rendered it susceptible to various network attacks. Conventional defense approaches face dual challenges: insufficient line-rate processing capacity and impractical online deployment requirements. The emergence of P4-based programmable data planes now enables line-speed traffic processing at the hardware level, creating new opportunities for protocol protection. In this context, we present MARS -a data plane-native TCP abuse detection and mitigation system that synergistically combines the Beaucoup traffic monitoring algorithm with artificial neural network (ANN) based anomaly detection, enhanced by adaptive heuristic mitigation rules. Through comprehensive benchmarking against existing TCP defense mechanisms, our solution demonstrates 12.95% higher throughput maintenance and 25.93% improved congestion window recovery ratio during attack scenarios. Furthermore, the proposed framework establishes several novel evaluation metrics specifically for TCP protocol protection systems.

As an important carrier of information transmission in online social networks (OSNs), the authenticity protection of images is of great significance. However, the abuse of image processing technology makes its security questionable. Meantime, lossy operations adopted by OSNs will change the forgery artifacts, which brings challenges to robust image forgery detection. To address this issue, considering the suppression of lossy noise caused by transmission, a novel multi-domain probability estimation network (PRest-Net) is proposed. Firstly, we design a multi-domain probability estimation method to capture the most differentiated regional information from the spatial, residual, and wavelet domains. Since the wavelet coefficient of semantic information is larger than that of lossy noise, and the edge texture can be highlighted in the residual image, the negative effect of lossy noise would be reduced and semantic forgery traces can be exposed more easily. We further design a forgery detector composed of low-level feature extraction, high-level feature extraction, and regional edge difference learning module, which can adaptively learn rich forgery clues. Extensive experimental results are provided to validate the superiority of PRest-Net compared with existing state-of-the-art detectors in the scenarios of detecting forged images transmitted over various OSNs.

The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (ReID) task. However, current ReID algorithms typically failed to effectively leverage the rich contextual information available, primarily due to their reliance on simplistic and coarse utilization of image attributes. Recent advances in artificial intelligence generated content have made it possible to automatically generate plentiful fine-grained attribute descriptions and make full use of them. Thereby, this paper explores the potential of using the generated multiple person attributes as prompts in ReID tasks with off-the-shelf (large) models for more accurate retrieval results. To this end, we present a new framework called Multi-Prompts ReID (MP-ReID), based on prompt learning and language models, to fully dip fine attributes to assist ReID task. Specifically, MP-ReID first learns to hallucinate diverse, informative, and promptable sentences for describing the query images. This procedure includes (i) explicit prompts of which attributes a person has and furthermore (ii) implicit learnable prompts for adjusting/conditioning the criteria used towards this person identity matching. Explicit prompts are obtained by ensembling generation models, such as ChatGPT and VQA models. Moreover, an alignment module is designed to fuse multi-prompts (i.e., explicit and implicit ones) progressively and mitigate the cross-modal gap. Extensive experiments on the existing attribute-involved ReID datasets, namely, Market1501 and DukeMTMC-reID, demonstrate the effectiveness and rationality of the proposed MP-ReID solution.

The escalating threat of hidden GPS tracking devices poses significant risks to personal privacy and security.Featured by their miniaturization and misleading appearances, GPS devices can be easily disguised in their surroundings making their detection extremely challenging.In this paper, we propose a novel side-channel-driven detection system, GPSBuster, leveraging electromagnetic radiation (EMR) emitted by GPS trackers.Our feasibility studies and hardware analysis reveal that unique EMR patterns associated with the tracker's operation, stemming from the quartz oscillator, local oscillator, and mixer in the Mixed-Signal on Chip (MSoC) system.Nevertheless, as a side-channel leakage, EMRs can be extremely weak and suffer from the ambient noise interference, rendering the detection impractical.To address these challenges, we develop the signal processing techniques with noise removals and a dual-dimensional folding mechanism to accumulate the spectrum energy and protrude the EMR patterns with high Signal-to-Noise Ratios (SNR).Our detection prototype, built with a portable HackRF One device, allows users to perform a scan-to-detect manner and achieves an overall success rate of 98.4% on top-10 selling GPS trackers under various testing cases.The maximum detection range is 0.61m.

Variable-length path queries are essential for finding paths in a graph that adhere to a specified length constraint, utilizing only edges with labels from a restricted subset of the edge labels. These queries play a crucial role in graph analytics and are supported by practical graph query languages like Cypher in property graph systems and SPARQL 1.1 in RDF graph systems. In this paper, we present a novel solution for efficient evaluation of variable-length path queries, based on worst-case optimal joins. Our solution's core relies on a jumping-like worst-case optimal join technique, allowing us to select a query vertex order that differs completely from existing graph systems based on worst-case optimal joins. Furthermore, we introduce a cost-based dynamic programming optimizer that combines traditional and jumping-like worst-case optimal join techniques. We also propose an optimization technique to leverage intra-query parallelism during query evaluation. Through extensive experiments conducted on numerous synthetic and real RDF and property graphs, we demonstrate that the proposed technique achieves excellent performance.

Fake images spread on online social networks can lead to public misconceptions, causing potential harm to politics, economics and social culture. Researching the authenticity of digital images is of great significance. Almost all online social networks perform multiple lossy operations on uploaded images, which will change the forgery traces and degrade the forgery detection performance of existing forensic methods. Therefore, forgery detection for images transmitted on online social networks has become a severe challenge. To solve this problem, we propose a multi-domain probability estimation network (PRest-Net). Specifically, considering that the wavelet coefficient of useful information is larger than that of lossy noise, and the image residual processing can highlight the edge texture, we design a multi-domain probability estimation method based on feature coupling and selective search to mine forgery traces in the spatial, residual, and wavelet domains. As a result, the lossy noise introduced by transmission on online social networks can be suppressed and the most differentiated regional information can be captured. Furthermore, a forgery detector based on low-level and high-level feature learning is proposed to adaptively capture rich operation clues and local edge differences between tampered and real areas, achieving fine-grained tampered region prediction. Extensive experiments show that the proposed PRest-Net outperforms existing state-of-the-art forgery detectors and is robust to online social network transmission.

Linux-based hypervisors in the cloud server suffer from an increasing number of vulnerabilities in the Linux kernel.To address these vulnerabilities in a timely manner while avoiding the economic loss caused by unplanned shutdowns, live patching schemes have been developed. Unfortunately, existing live patching solutions have failed to protect patches from post-deployment attacks. In addition, patches that involve changes to global variables can lead to practical issues with existing solutions. To address these problems, we present FortifyPatch, a tamper-resistant live patching solution for Linux-based hypervisors in cloud environments. Specifically, FortifyPatch employs multiple Granule Protection Tables from Arm Confidential Computing Architecture to protect the integrity of deployed patches. TrustZone Address Space Controller and Performance Monitor Unit are used to prevent the bypassing of the Patch via kernel code protection and timely page table verification. FortifyPatch is also able to patch global variables via well-designed data access traps.We prototype FortifyPatch and evaluate it using real-world CVE patches. The result shows that FortifyPatch is capable of deploying 81.5% of CVE patches. The performance evaluation indicates that FortifyPatch protects deployed patches with 0.98% and 3.1% overhead on average across indicative benchmarks and real-world applications, respectively.

Realistic co-speech gestures are important to anthropomorphize ECAs, as nonverbal behavior improves expressiveness of their speech greatly. However, the existing approaches to generating co-speech gestures with sufficient details (including fingers, etc.) in 3D scenarios are indeed rare. Additionally, they hardly address the problem of abnormal gestures, temporal–spatial coherence and diversity of gesture sequences comprehensively. To handle abnormal gesture issues, we put forward an angle conversion method to remove body part length from the original in-the-wild video dataset via transferring coordinates of human upper body key points into relative deflection angles and pitch angles. We also propose a neural network called HARP with encoder–decoder architecture to transfer MFCC featured speech audio into aforementioned angles on the basis of CNN and LSTM. The angles then can be rendered as corresponding co-speech gestures. Compared with the other latest approaches, the co-speech gestures generated by HARP are proved to be almost as good as the real person, i.e., they have strong temporal–spatial coherence, diversity, persuasiveness and credibility. Our approach puts finer control on co-speech gestures than most of the existing works by handling all key points of the human upper body. It is more feasible for industrial application, since HARP can be adaptive to any human upper body model. All related code and evidence videos of HARP can be accessed at https://github.com/drrobincroft/HARP .

With an increasing amount of data produced by pervasive and ubiquitous smart devices, many Internet of Things (IoT) applications adopt the cloud platform to store and process data. To protect data security and privacy, attribute-based encryption (ABE) has been widely used in cloud-assisted IoT systems. However, most ABE schemes usually require a central authority to distribute decryption keys for all users, which may raise security and efficiency concerns; in addition, the exposure of decryption keys would severely damage the data privacy. In this article, we introduce a novel notion of decentralized attribute-based puncturable encryption (DABPE). DABPE allows data owner to generate public and secret keys by himself, without relying on any central authority. When outsourcing data to the cloud, the data owner can encrypt data with an access policy; moreover, the data owner could issue particular keys for different data users and only those users whose keys satisfy the access policy can access the data. To achieve a flexible forward security, the data owner and data user can update their keys with some tags such that the data specified by the tags would not be revealed even if the keys are disclosed. We design a concrete DABPE scheme and prove its security in the standard model, and also conduct extensive experiments to show the efficiency of the proposed scheme.

Deep-Learning-as-a-service (DLaaS) has received increasing attention due to its novelty as a diagram for deploying deep learning techniques. However, DLaaS faces performance and security issues that urgently need to be addressed. Given the limited computation resources and concern of benefits, Quality-of-Service (QoS) metrics should be revised to optimize the performance and reliability of distributed DLaaS systems. New users and services dynamically and continuously join and leave such a system, resulting in cold start issues, and additionally, the increasing demand for robust network connections requires the model to evaluate the uncertainty. To address such performance problems, we propose in this article a deep learning-based model called embedding enhanced probability neural network, in which information is extracted from inside the graph structure and then estimated the mean and variance values for the prediction distribution. The adversarial attack is a severe threat to model security under DLaaS. Due to such, the service recommender system's vulnerability is tackled, and adversarial training with uncertainty-aware loss to protect the model in noisy and adversarial environments is investigated and proposed. Extensive experiments on a large-scale real-world QoS dataset are conducted, and comprehensive analysis verifies the robustness and effectiveness of the proposed model.

In this article, we explore intercity mobility in urban agglomerations by surveying people traveling across cities based on private car trajectory data. Specifically, we first adopt the statistical analysis method to mine the intercity mobility in terms of various metrics of travel trips, so as to gain a preliminary understanding of intercity mobility in urban agglomeration. Then, we utilize the tensor decomposition method to conduct in-depth study on the intercity mobility pattern from the perspectives of complexity and multidimensionality. We construct a 4-D tensor based on private car trajectory and point-of-interest (POI) datasets and define the functional similarity and geographic adjacency between regions. Finally, we design an alternating proximal gradient (APG)-based method to resolve the core tensor and factor matrix, leading to the fine-grained discovery of intercity mobility patterns on administrative divisions in the urban agglomeration. Extensive experiments are conducted to evaluate the analysis of intercity mobility, using a real-world dataset containing one-year private car trajectories from five cities in the selected urban agglomeration. The experiments show that the proposed method successfully captures 20 intercity mobility patterns, in which the factor matrices retrieve the patterns from different dimensions with core tensors characterizing correlations between patterns in factor matrices. Besides, the extracted intercity mobility patterns not only cover administrative areas with frequent intercity interactions, but also contain areas with less intercity interactions. It validates that the intercity mobility is consistent with the regional functions in urban agglomeration.

In spatial crowdsourcing, location-based task recommendation schemes are widely used to match appropriate workers in desired geographic areas with relevant tasks from data requesters. To ensure data confidentiality, various privacy-preserving location-based task recommendation schemes have been proposed, as cloud servers behave semi-honestly. However, existing schemes reveal access patterns, and the dimension of the geographic query increases significantly when additional information beyond locations is used to filter appropriate workers. To address the above challenges, this paper proposes two efficient and privacy-preserving location-based task recommendation (EPTR) schemes that support high-dimensional queries and access pattern privacy protection. First, we propose a basic EPTR scheme (EPTR-I) that utilizes randomizable matrix multiplication and public position intersection test (PPIT) to achieve linear search complexity and full access pattern privacy protection. Then, we explore the trade-off between efficiency and security and develop a tree-based EPTR scheme (EPTR-II) to achieve sub-linear search complexity. Security analysis demonstrates that both schemes protect the confidentiality of worker locations, requester queries, and query results and achieve different security properties on access pattern assurance. Extensive performance evaluation shows that both EPTR schemes are efficient in terms of computational cost, with EPTR-II being$10^{3}\times$faster than the state-of-the-art scheme in task recommendation.

Trajectory recovery aims to restore missing data for reconstructing high-quality human mobility trajectory, which benefits a wide range of intelligent transportation system applications ranging from urban planning to travel recommendation. Inspired by the inherent regularity of human mobility, existing approaches capture spatial-temporal transition regularities in historical trajectory for data recovery. Although promising, existing solutions suffer from two limitations.i)These methods fail to recover occasionally-visited points (OVP) due to the lack of semantic information when learning spatial-temporal transition regularities.ii)The information before and after missing data is not be fully utilized for trajectory recovery. To overcome the limitations, we propose a novel semantic-aware trajectory recovery framework. First, we leverage heterogeneous information network (HIN) to encode various semantic correlations for obtaining rich semantic embeddings, which are fused with temporal information to form spatial-temporal semantic context. Then, we develop a behavior attention mechanism to capture semantic behavior transition regularities for trajectory recovery based on the bidirectional spatial-temporal semantic context before and after missing data. Extensive experiments on four real-world datasets show that our proposed method outperforms the state-of-the-arts by 7%-11% in term of recall, F1-score and mean average precision.

The accurate prediction of traffic conditions, including speed, flow, and travel time, poses a critical challenge in urbanization that significantly impacts car owners and road administrators. However, in certain scenarios with restricted road data availability (e.g. lack of traffic light status and signal control strategies, cooperation between road administrators and third parties, etc.), it is imperative to make effective use of basic road information (e.g. historical traffic data and road connectivity) to improve both prediction accuracy and scalability on various arterial road networks against state-of-art deep learning models. In this paper, we propose a spatial-temporal learning framework TPGraph for an accurate prediction of arterial roads’ traffic data by effectively utilizing upstream and downstream road information. TPGraph is composed of three major parts: 1) A multi-scale temporal feature fusion module that utilizes a multi-head attention mechanism to integrate recently-periodic features, daily-periodic features, and weekly-periodic features; 2) A multi-graph convolution module that employs graph fusion and graph convolution networks to capture richer spatial semantics, and 3) A dynamic spatial-temporal prediction module that leverages a spatial-temporal transformer for single or multiple traffic-state predictions. Our proposed framework, TPGraph, leverages just multi-scale historical traffic conditions and readily accessible spatial factors as input to generate accurate predictions of future traffic conditions. We mainly evaluate the performance of our approach through multi-step prediction experiments conducted at hourly intervals, forecasting travel time or travel speed for each road at 15 mins, 30 mins, and 1 hour. Furthermore, we conduct extensive experiments on real-world arterial road datasets to demonstrate the superior predictive performance of TPGraph compared to existing methods.

We propose to design vertex encoding for determinations of no-result edge queries that should not be executed. Edge query is one of the core operations in mainstream graph databases, which is to retrieve edges connecting two given vertices. Real-world graphs may be too large to be stored in memory and frequently accessing edge data on disk usually incurs much overhead. The average degree of real-world graph tends to be much less than the vertex number, and edges may not exist in most pairs of vertices. Efficiently avoiding no-result edge query executions will certainly improve the performance of graph database. In this paper, we propose a new and important problem for determining no-result edge queries: vertex encoding for edge nonexistence determination (VEND, for short). We build a low dimensional vertex encoding for all vertices, and we can efficiently determine most vertex pairs that are connected by no edges just with their corresponding codes. The encoding can be efficiently adjusted when data updates happen. With VEND, we can utilize in-memory efficient operations to filter no-result disk accesses for edge query. We also design SIMD-oriented compression optimizations to further improve performance. Extensive experiments on real-world datasets confirm the effectiveness of our solution.

Internet of Things (IoT) devices play essential roles in both industry and daily scenarios. However, unlike smartphones and computers, IoT devices typically lack conventional user interfaces (UIs) such as keyboards and touchscreens. It renders the traditional user authentication designs, e.g., PINs and patterns, inapplicable. In this article, we proposeTouchAccessthat enables users to unlock an arbitrary IoT device by applying a simple touch. Our design is motivated by the key observation that IoT devices unavoidably generate electromagnetic emanations (EMM) while they are functioning. When the user touches the device, it causes time-varying coupling between those two and generates unique EMMs. Our feasibility studies further reveal that thesehuman-induced EMMsare distinct and strongly correlated with the circuitry properties of the user and the device, but are susceptible to environmental EM noises, thus lowering the authentication accuracy. To address this challenge, we develop signal processing techniques with a Siamese network learning scheme that clears the ambient electromagnetic (EM) noises, extracts robust signal features, and builds noise-resistant classifiers, enabling users to be correctly recognized. A significant advantage ofTouchAccessis that it requires only a low-cost analog-to-digital converter (ADC) to sense the EM signal. We implementTouchAccesson commercial off-the-shelf (COTS) IoT devices, which vary significantly in terms of UIs, sizes, and hardware designs. The performance evaluations show thatTouchAccessachieves an average authentication accuracy as high as 97.85%.

Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7% and 39.3% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.

The popularity of IoT devices has penetrated our daily life while posing new challenges in user authentication. Today’s solutions, e.g., passwords, fingerprints, and FaceIDs, primarily rely on specialized sensors or user interfaces to collect user’s identification information, which may not universally exist on heterogeneous IoT devices. In this paper, we propose a novel user authentication method that exploits the Electromagnetic (EM) emanations radiated from IoT devices. Our design is motivated by the observation that human touches on the IoT device can lead to time-varying coupling between these two. Consequently, it impacts the device’s EM emanations that can be picked up by its inertial ADC (analog-to-digital converter) interfaces. We ask the user to tap on the device rhythmically following a self-determined melody, such that the human-coupled EM emanations vary accordingly. We thus extract the rhythm pattern as the user’s secure password named EM-Rhythm . To examine its effectiveness, EM-Rhythm is implemented on a wide range of IoT devices. We show that our scheme achieves authentication accuracy as high as 98.67% with less than three login attempts. Besides, it is robust against various types of attacks and maintains stable performances under various settings. EM-Rhythm also exhibits satisfactory usability in terms of memorability and time consumption.

We propose to design vertex encoding for determinations of no-result edge queries that should not be executed. Edge query is one of the core operations in mainstream graph databases, which is to retrieve the corresponding edges connecting two given vertices. Real-world graphs may be too large to be stored in memory and frequently accessing edge data on disk usually incurs much overhead. Average degree of real-world graph tends to be much less than the vertex number, and edges may not exist in most pairs of vertices. Efficiently avoiding no-result edge query executions will certainly improve performance of graph database. In this paper, we propose a new and important problem for determining no-result edge queries: vertex encoding for edge nonexistence determination (VEND, for short). We build a low dimensional vertex encoding for all vertices, and we can efficiently determine most vertex pairs that are connected by no edges just with their corresponding codes. With VEND, we can utilize in-memory efficient operations to filter no-result disk accesses for edge query. We also design maintenance algorithms for the proposed solution when data updates happen. Extensive experiments on many real-world datasets confirm the ability of our solution on determining a quite high proportion of non-edge vertex pairs, as well as the acceleration for edge queries.

The searchable encryption (SE) is a particular case of structured encryption, which has been intensively researched in the secure cloud storage system. By constructing a structured secure index, such as encrypted multimaps (EMMs), encrypted inverted index (EII), etc., SE can achieve efficient keyword search over the encrypted data set. However, existing SE constructions do not take search permissions into consideration, resulting in the lack of a mechanism of the data access control, which may not be suitable for Industrial Internet of Things (IIoT) applications, since an integrated industrial system contains all kinds of data with rigorous access permissions. In this article, we construct an attribute-based SE (ABSE) construction for a cloud-assisted IIoT application scenario. By designing the novel access policy-based structured secure index and the attribute-based search token, our construction achieves fine-grained keyword search privilege control over encrypted IIoT data as well as the same search complexity as the traditional SE. To the best of our knowledge, this is the first ABSE construction. We provide the correctness and security proofs for our construction. Experimental evaluation results in a real-world data set show the correctness and the practical search efficiency of the proposed ABSE.

RDF (Resource Description Framework) is a model widely used to construct knowledge bases, while SPARQL (SPARQL Protocol and RDF Query Language) is the standardized structured query language to manipulate RDF data. Recently, many data providers have published their RDF datasets in their own autonomous sites and provided SPARQL query interfaces, calledRDF sources. In order to integrate multiple RDF sources, researchers put forward the federated RDF system to support the federated SPARQL queries. However, existing studies can only support efficient basic queries but not top-k queries. Toward this end, we propose a cost-driven top-k queries optimization approach in federated RDF systems, which can support both top-k queries for single variable ordering and expression ordering. Firstly, we propose an optimized query decomposition method to decompose the federated query into multiple subqueries. Secondly, while considering the top-k operator, we propose a cost model to evaluate the query cost and join cost of subqueries. The optimal query plan can be obtained by the costed-based query plan generation algorithm. Finally, combined with the characteristics of top-k queries, an incremental query plan execution strategy is developed to minimize the total query cost. Experimental results show that the proposed method is effective, efficient and scalable.

The issue of keyword search in federated resource description framework(RDF) systems is revisited in this paper. Numerous independent SPARQL endpoints that solely offer SPARQL query interfaces make up a federated RDF system. Others find it challenging to remotely build up the global inverted indices over the full RDF network by downloading the dataset at various SPARQL endpoints. This poses a challenge for existing keyword search approaches. In this paper, we propose a keyword search approach for federated RDF systems without building up global inverted indices on the entire RDF graph. We build an offline schema graph for the federated RDF system. During query evaluation, the full-text search interfaces provided by existing SPARQL endpoints were used to map keywords to vertices in the schema graph. Subsequently, the schema graph was explored to construct SPARQL queries from the keywords. The constructed queries were evaluated over the underlying federated RDF systems. Some cost models were proposed to measure keyword mapping and query construction. On the other hand, to further improve the efficiency, a multiple query optimization strategy was designed to rewrite the queries and share the common computation during evaluation. Extensive experiments on real RDF datasets show that the proposed techniques are effective.

Image forgery detection has aroused widespread research interest in both academia and industry because of its potential security threats. Existing forgery detection methods achieve excellent tampered regions localization performance when forged images have not undergone post-processing, which can be detected by observing changes in the statistical features of images. However, forged images may be carefully post-processed to conceal forgery boundaries in a particular scenario. It becomes tough challenging to these methods. In this paper, we perform an analogous analysis between image forgery detection and blind signal separation, and formulate the post-processed image forgery detection problem into a signal noise separation problem. We also propose a signal noise separation-based (SNIS) network to solve the problem of detecting post-processed image forgery. Specifically, we first adopt the signal noise separation module to separate tampered region from the complex background region with post-processing noise, which weakens or even eliminates the negative impact of post-processing on forgery detection. Then, the multi-scale feature learning module uses a parallel atrous convolution architecture to learn high-level global features from multiple perspectives. Besides, a feature fusion module is utilized to enhance the discriminability of tampered regions and real regions by strengthening the boundary information. Finally, the prediction module is designed to predict the tampered region and classify the type of tampering operation. Extensive experiments show that the proposed SNIS is not only effective for forgery detection on forged images without post-processing, but also promising in robustness against multiple post-processing attacks. Furthermore, SNIS is robust in detecting forged images from unknown sources.

Cloud computing has become an increasingly popular option for users to store and share data. Encryption prior to outsourcing data to the cloud is the best way to protect data security and privacy; however, it hinders sharing of the data that was encrypted. In addition, users in many real-world organizations (e.g., enterprises) have multiple level structures and a higher-level user should have the privilege to decide which data can be shared with a lower-level user. Most solutions in the literature suffer from inefficiency or inflexibility in tackling this problem. In this article, we propose a fine-grained hierarchical data sharing (FHDS) scheme in clouds. With FHDS, the data owner can encrypt data with his public key, and then selectively share encrypted data with users in a hierarchy; if necessary, the users can disseminate the owner's data to their subordinates in the lower levels by generating access keys. In particular, the higher-level users could puncture the keys with some tags such that the part of the owner's data which is labeled by the punctured tags will not be accessible to the lower-level users. The proposed scheme is provable secure under our security model and performance analyses show the efficiency of the scheme.

The cloud-assisted industrial Internet of Things (IIoT) architecture can sustain highly available computation and massive storage services for modern industrial systems. When data owners store IIoT data to remote cloud platforms, the data security will face tough challenges. Cryptographic technologies endow an ability to guarantee data confidentiality. However, traditional encryption techniques make data access control and data searching malfunctioning. Recently emerging attribute-based keyword search (ABKS) primitive achieves fine-grained access control and effective data searching over ciphertexts. However, existing ABKS schemes only consider single data owner scenarios and may be an inappropriate choice for IIoT applications, where there exists multiple data owners for an integrated industrial system. Directly extending state-of-the-art single owner schemes to ones for multiowner environment will impose a complicated key management issue. We present an ABKS scheme for multiowners in the cloud-assisted IIoT architecture. By designing a novel master key generation and private key aggregation mechanism with desired communication overheads, our scheme eliminates the complex key management issue in the multiowner model. Formal security proof demonstrates that our scheme is secure against the cloud server. Experimental evaluations also demonstrate its correctness and practicality.

Traffic flow parameters are an important data support for the research and development of several technologies in the intelligent transportation system. Therefore, accurate and real-time estimation of traffic flow is particularly important for urban traffic. In this study, a real-time traffic flow detection system framework was constructed based on video image collection and analysis. According to the vehicle detection and tracking results, a traffic flow parameter estimation model and an improved LSTM network are proposed for spatiotemporal counting feature recognition. The results conclude that the developed framework can estimate the traffic flow density and count vehicles, as well as estimate the traffic flow velocity and traffic volume to estimate and optimize traffic flow, respectively. Additionally, the simulation results show that the proposed method can not only counts the two-way traffic vehicles quickly and accurately, but also avoids the use of the complex multi-target tracking method to spatiotemporal correlation of a single target, increases the speed and accuracy of the spatiotemporal information processing procedure, and has stronger scene adaptability.

The attribute-based keyword search (ABKS), which simultaneously achieves searching and fine-grained access control over encrypted data, is frequently applied in cloud computing environments characterized by data storage and sharing. Recently, inspired by attribute-based encryption (ABE) and searchable encryption (SE) primitives, several ABKS schemes have been presented. However, almost all existing ABKS schemes actually only provide an attribute-based keyword equality match function and do not have a structural index to support practical search efficiency and dynamic data updates in real-world applications. To the best of our knowledge, this study is the first to realize an attribute-based keyword search construction supporting numerical comparison expressions with the practical search efficient and dynamic data update capacity (ABKS-NICEST), based on our proposed attribute-based keyword secure search scheme supporting numerical comparison expressions (ABKS-NICE) and anexclusive OR-chain-based inverted index structure. To the best of our knowledge, ABKS-NICEST is the first attributed-based keyword search scheme with practical search efficiency and dynamic data update capacity. In addition, numerical values are an important and common attribute, so providing comparison expressions among numerical values can greatly enhance the expressivity of access policy. Therefore, we use the prefix membership verification technique to design a method to support any numeric comparison expression in a flexible and uniform manner. Through theoretical and experimental evaluations, we determine that ABKS-NICEST is the most efficient ABKS scheme.

Deepfake has ignited hot research interests in both academia and industry due to its potential security threats. Many countermeasures have been proposed to mitigate such risks. Current Deepfake detection methods achieve superior performances in dealing with low-visual-quality Deepfake media which can be distinguished by the obvious visual artifacts. However, with the development of deep generative models, the realism of Deepfake media has been significantly improved and becomes tough challenging to current detection models. In this paper, we propose a frame inference-based detection framework (FInfer) to solve the problem of high-visual-quality Deepfake detection. Specifically, we first learn the referenced representations of the current and future frames’ faces. Then, the current frames’ facial representations are utilized to predict the future frames’ facial representations by using an autoregressive model. Finally, a representation-prediction loss is devised to maximize the discriminability of real videos and fake videos. We demonstrate the effectiveness of our FInfer framework through information theory analyses. The entropy and mutual information analyses indicate the correlation between the predicted representations and referenced representations in real videos is higher than that of high-visual-quality Deepfake videos. Extensive experiments demonstrate the performance of our method is promising in terms of in-dataset detection performance, detection efficiency, and cross-dataset detection performance in high-visual-quality Deepfake videos.

The physical layer secret key generation scheme is a preferred solution designed for resource-constrained Internet of Things (IoT) devices. But it suffers from a severe attack, the signal manipulating attack, which aims at controlling the generated key. The existing solutions either cannot prevent all kinds of signal manipulation attacks or require working in full-duplex mode, which is not suitable for resource-constrained IoT devices. In this article, we introduce a secret key generation scheme with the help of an untrusted relay to address this dilemma. Also, our method can protect the privacy of legitimate users from the untrusted relay. We conclude a general signal manipulation attack model from existing practical signal manipulation attacks and analyze the security strength and privacy preserving ability of our scheme based on this model. Finally, we compare our method with existing signal manipulation attack solutions. The result shows that our method is the best solution for resource-constrained IoT systems.

With the proliferation of cloud computing, data owners can outsource the spatial data from the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage resources and location-based services. However, the outsourced services may raise privacy concerns, since the cloud server may not be fully trusted for both data owners and search users. If the data owners and search users conventionally encrypt the spatial data and query requests, the efficiency and functionality of query processing are weakened. Most of the existing works only focus on spatial data search or keyword search and do not consider spatial keyword search over encrypted data. In this article, we first design a geometric range query (GRQ) scheme, which can generate an arbitrary geometric range to fit the search user’s desired spatial data while protecting location privacy. Furthermore, based on GRQ, we propose a multidimensional spatial keyword similarity search scheme with access control (MSSAC) by integrating the polynomial function and matrix transformation. Specifically, an access control strategy is defined by a role-based polynomial function, which is embedded in the vectors of indices and trapdoors to achieve efficient and lightweight access control. Moreover, MSSAC enables the cloud server to execute compute-then-compare operations for spatial keyword search in a privacy-preserving manner by leveraging techniques of randomizable permutation and matrix multiplication. The formal security analyses and extensive experiments demonstrate that GRQ and MSSAC preserve the privacy of data owners and search users while achieving efficient spatial keyword search.

Software-Defined Networking (SDN) is an emerging network architecture. The decoupled data and control plane provides programmability for efficient network management. However, the centralized control mode of SDN also exposes unique vulnerabilities. Low-rate Denial of Service (LDoS) has a lower attack rate than ordinary DDoS attacks with the characteristics of periodicity and concealment, which is among one of the severe threats to SDN. In this paper, we propose a lightweight, real-time framework Performance and Features (P&F) to detect and mitigate LDoS attacks with SDN. We implement LDoS attacks in SDN, extract traffic features with OpenFlow, and classify the features into two categories. By analyzing the performance (P) of normal traffic under attack state, P&F determines whether LDoS attacks take effect based on machine learning. Meanwhile, P&F tries to locate attack sources and victims according to flow features (F) of LDoS attacks based on time-frequency analysis. According to detection and locating results, P&F sets corresponding mitigation schemes. Experimental results show that P&F has a high detection rate and low false positive rate for detecting LDoS attacks. P&F can deploy on controllers to achieve real-time attack detection and mitigation with low system cost, which can defend against LDoS attacks effectively.

The development of technologies that can generate Deepfake videos is expanding rapidly. These videos are easily synthesized without leaving obvious traces of manipulation. Though forensically detection in high-definition video datasets has achieved remarkable results, the forensics of compressed videos is worth further exploring. In fact, compressed videos are common in social networks, such as videos from Instagram, Wechat, and Tiktok. Therefore, how to identify compressed Deepfake videos becomes a fundamental issue. In this paper, we propose a two-stream method by analyzing the frame-level and temporality-level of compressed Deepfake videos. Since the video compression brings lots of redundant information to frames, the proposed frame-level stream gradually prunes the network to prevent the model from fitting the compression noise. Aiming at the problem that the temporal consistency in Deepfake videos might be ignored, we apply a temporality-level stream to extract temporal correlation features. When combined with scores from the two streams, our proposed method performs better than the state-of-the-art methods in compressed Deepfake videos detection.

Cloud storage services allow data owners to outsource their potentially sensitive data (e.g., private genome data) to remote cloud servers in a ciphertext form. To enable data owners to further share the data encrypted in ciphertexts, many proxy re-encryption (PRE) schemes are proposed. However, most schemes only support single-recipient or coarse-grained re-encryption, which may limit the flexibility for data sharing. To address this issue, we propose a Policy-based Broadcast Access Authorization (PBAA) scheme by introducing the well-established identity-based broadcast encryption (IBBE) and key-policy attribute-based encryption into PRE. In our PBAA scheme, a data owner can apply IBBE to encrypt his data to a group of recipients. More importantly, the data owner can generate a delegation key with an access policy, and send this key to the cloud such that it can convert any initial ciphertext satisfying the access policy into a new ciphertext for a new group of recipients. With these features, cloud users can share their remote data in a secure and flexible way. Security analysis and performance evaluation show that the PBAA scheme is secure and efficient, respectively.

With the coming era of cloud technology, cloud storage is an emerging technology to store massive digital images, which provides steganography a new fashion to embed secret information into massive images. Specifically, a resourceful steganographer could embed a set of secret information into multiple images adaptively, and share these images in cloud storage with the receiver, instead of traditional single image steganography. Nevertheless, it is still an open issue how to allocate embedding payload among a sequence of images for security performance enhancement. This paper formulates adaptive payload distribution in multiple images steganography based on image texture features and provides the theoretical security analysis from the steganalyst's point of view. Two payload distribution strategies based on image texture complexity and distortion distribution are designed and discussed respectively. The proposed strategies can be employed together with these state-of-the-art single image steganographic algorithms. The comparisons of the security performance against the modern universal pooled steganalysis are given. Furthermore, this paper compares the per image detectability of these multiple images steganographic schemes against the modern single image steganalyzer. Extensive experimental results show that the proposed payload distribution strategies could obtain better security performance.

The newly emerging language-based video moment retrieval task aims at retrieving a target video moment from an untrimmed video given a natural language as the query. It is more applicable in reality since it is able to accurately localize a specific video moment, as compared to traditional whole video retrieval. In this work, we propose a novel solution to thoroughly investigate the language-based video moment retrieval issue under the adversarial learning. The key of our solution is to formulate the language-based video moment retrieval task as an adversarial learning problem with two tightly connected components. Specifically, a reinforcement learning is employed as a generator to produce a set of possible video moments. Meanwhile, a multi-task learning is utilized as a discriminator, which integrates inter-modal and intra-modal in a unified framework by employing a sequential update strategy. Finally, the generator and the discriminator are mutually reinforced in the adversarial learning, which is able to jointly optimize the performance of both video moment ranking and video moment localization. Extensive experimental results on two challenging benchmarks, i.e., Charades-STA and TACoS datasets, have well demonstrated the effectiveness and rationality of our proposed solution. Meanwhile, on the larger and unbiased datasets, i.e., ActivityNet Captions and ActivityNet-CD, our proposed framework exhibits excellent robustness.

It is becoming fashionable for people to access data outsourced to clouds with mobile devices. To protect data security and privacy, attribute-based encryption (ABE) has been widely used in cloud storage systems. However, one of the main efficiency drawbacks of ABE is the high computation overheads at mobile devices during user revocation and file access. To address this issue, we propose a revocable attribute-based data storage (RADS) scheme equipped with several attracting features. First, our RADS scheme achieves a fine-grained access control mechanism, by which file owners do not need to explicitly specify authorized visitors to their outsourced files. Second, our RADS scheme allows mobile users to authorize the cloud service provider (CSP) to share costly computations in file access, without exposing the file content. Third, our RADS scheme offloads the operations of access-credential update and file re-encryption during revocation process to CSP, leaving all non-revoked users undisturbed. The revocation of RADS achieves a strong data protection, i.e., revoked users can access neither newly uploaded files nor old ones. The security and efficiency of the RADS scheme are validated via both analysis and experimental results.

The software-defined network (SDN) has created the conditions for the optimization and development of network structures. However, its architecture is still not sufficient to resist or identify all denial of service (DoS) attacks, such as low-rate DoS (LDoS) attacks. Due to their low transporting rate and flash-crowd-like nature, LDoS attacks are well hidden in the background traffic and difficult to identify by anti-DoS mechanisms in the SDN. By implementing LDoS attacks in the SDN, we confirm that they can severely degrade the quality of service. We further propose a framework based on the histogram-based gradient boosting and finding peaks (HGB-FP) algorithm to detect LDoS attacks and mitigate their influence in the SDN in real-time. The histogram-based gradient boosting (HGB) algorithm, an ensemble learning with high quality and low complexity, can identify LDoS attacks quickly and accurately. The finding peaks (FP) algorithm locates the attacker via peak properties of the flow and installs flow rules on the switches to drop the attack flows. Experiments prove that our framework has higher accuracy and F-measure in identifying LDoS attacks than other machine learning approaches and mitigates the impact of LDoS attacks on bottleneck links in the SDN within seconds on average.

Given an untrimmed video and a query sentence, cross-modal video moment retrieval aims to rank a video moment from pre-segmented video moment candidates that best matches the query sentence. Pioneering work typically learns the representations of the textual and visual content separately and then obtains the interactions or alignments between different modalities. However, the task of cross-modal video moment retrieval is not yet thoroughly addressed as it needs to further identify the fine-grained differences of video moment candidates with high repeatability and similarity. Moveover, the relation among objects in both video and sentence is intuitive and efficient for understanding semantics but is rarely considered.Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates. Specifically, we first introduce a visual relational graph and a textual relational graph to form relation-aware representations via message propagation. Thereafter, a multi-task pre-training is designed to capture domain-specific knowledge about objects and relations, enhancing the structured visual representation after explicitly defined relation. Finally, the graph matching and boundary regression are employed to perform the cross-modal retrieval. We conduct extensive experiments on two datasets about daily activities and cooking activities, demonstrating significant improvements over state-of-the-art solutions.

Recently, many forensic techniques have been developed to detect the use of a certain processing operation. When utilizing several manipulations to alter an image, artifacts left by manipulations that have been applied later can potentially disguise traces left by manipulations that were applied earlier. Therefore, the detection of manipulations become difficult. In this paper, we focus on identifying the manipulations in an image operation chain composed of multiple manipulations in a certain order. To address this issue, we analyze the relationship between manipulations identification and blind signal separation. Then, we propose a features decoupling method based on blind signal separation, which decouples the coupled features due to the superimposed processing artifacts and exploits the decoupled features to identify multiple operations. The experiments carried out on two image operation chains confirm the effectiveness of the proposed method.

Multi-keyword search, aiming to search the objects by a query request that consists of multiple keywords, has wide applications in personalized recommendation services. Mean-while, the fast development of cloud technology has given rise to a new trend that data are encrypted before being outsourced to a public cloud for users to enjoy pay-as-you-go services. However, most of the existing works primarily focus on the single keyword search, and consider a general scenario with a single owner and a single user. In this paper, we propose an efficient and Privacy-preserving Multi-user Multi-keyword Search (PMMS) scheme, which can support user scalability without key sharing. In particular, based on the matrix decomposition, a key derivation approach is integrated into our PMMS to generate secret keys and re-encryption keys. Furthermore, by employing threshold predicate encryption and leveraging the techniques of matrix transformation and proxy re-encryption, PMMS guarantees that only the comparison result of an inner product of two vectors and a pre-defined threshold is revealed, and enables the cloud server to perform multi-keyword search in an efficient and privacy-preserving manner. Security analysis shows that the confidentiality of owners’ data and users’ queries can be guaranteed. Extensive experiments on a real-world dataset demonstrate that PMMS is efficient in terms of multi-keyword search.

Data sharing is one of the key technologies, which provides the practice of making data collected from a crowd of mobile devices available to others using a cloud infrastructure, known as mobile crowdsensing (MCS). However, the collected data may contain sensitive information, and sharing them in public clouds without proper protection could cause serious security problems, such as privacy leakage, unauthorized access, and secret key abuse. To address the above issues, in this paper, we propose a Traceable and privacy-preserving non-Interactive Data Sharing (TIDS) scheme in mobile crowdsensing. Specifically, to achieve privacy-preserving fine-grained data sharing, an attribute-based access policy is generated by a data owner without interacting with data users in the TIDS. Furthermore, we design a ciphertext conversion mechanism to support flexible data sharing. Also, by utilizing traceable Ciphertext-Policy Attribute-Based Encryption (CP-ABE), TIDS supports a trusted authority to trace malicious users who abuse their secret keys without incurring additional computational overhead. Security analysis demonstrates that TIDS can protect the confidentiality of the outsourced data. Experimental results show that TIDS can achieve efficient data sharing in mobile crowdsensing applications.

Low-rate denial of service (LDoS) attack is a derivative denial of service (DoS) attack, which reduces the quality of service in the network in the means of sending high-strength and instantaneous streams of data in cycles. Based on the above attack, we proposed a method based on adaptive shared nearest neighbor clustering and outlier factor analysis (ASNNC-OFA) to detect it, whose core idea is to calculate the traffic characteristics of network. We divide these traffic characteristic data based on shared neighbor clustering, and use the outlier factor algorithm to perform anomaly analysis on the divided data by training threshold. The experiments were carried out on NS2 and test-bed respectively, the results of them show that the detection method we proposed has low false negative rate and false positive rate, so it can effectively detect LDoS attacks.

Due to the increasing healthcare costs and the advance of wireless technology, health monitoring systems have been widely adopted recently. In health monitoring systems, a hospital outsources a clinical decision model to a cloud service provider, which receives biomedical data from remote clients and produces clinical decisions based on the outsourced model. Due to critical privacy concerns, both the clinical decision model and biomedical data should be protected. In this article, we propose an efficient and privacy-preserving decision tree (PPDT) classification scheme for health monitoring systems. Specifically, we first transform a decision tree classifier (i.e., the clinical decision model) into the Boolean vectors. Then, we leverage symmetric key encryption to encrypt the Boolean vectors as encrypted indices. The PPDT classification is achieved by searching the encrypted indices with encrypted tokens. We formulate a leakage function and provide the security definition and simulation-based proof for PPDT. The performance analyses demonstrate that PPDT is very efficient in terms of computation, communication, and storage. Experimental evaluations show that PPDT only requires microsecond-level execution time, kilobyte-level communication costs, and kilobyte-level storage costs on the test data set.

In cloud-based health monitoring services, support vector machine (SVM) classification techniques are often utilized by medical institutes to build medical decision models, which can be outsourced to a cloud server for producing medical decisions based on medical features from remote clients. In this article, we propose a verifiable and secure SVM classification scheme ($\mathsf {VSSVMC}$) for cloud-based health monitoring services in a malicious setting, where the cloud server may return invalid decisions. By constructing verifiable indices,$\mathsf {VSSVMC}$ensures the verifiability of medical decisions, which enables clients to detect whether the cloud server returns incorrect or incomplete medical decisions. Symmetric key encryption is leveraged to ensure the confidentiality of the medical decision model and medical data with computational efficiency. We give security and verifiability definitions and provide formal security and verifiability proofs for$\mathsf {VSSVMC}$. Performance analyses show that$\mathsf {VSSVMC}$is extremely efficient in terms of computation, communication, and storage. Experimental evaluations demonstrate that$\mathsf {VSSVMC}$achieves microsecond-level execution time with kilobyte-level communication and storage overheads on the tested data set.

Support vector machine (SVM) classification techniques have been widely adopted for building clinical decision models. In cloud-based remote clinical decision services, a healthcare center outsources the clinical decision model to a cloud server, which then provides remote clinical decision services to end users. In this article, we propose a practical and secure SVM classification scheme (${\sf SSVMC}$) for cloud-based remote clinical decision services. Specifically, we first extract SVM decision rules from an SVM classifier. Then, we leverage symmetric key encryption to protect the confidentiality of medical data and prevent the cloud service provider from misusing intellectual property of the outsourced clinical model. Finally, we build encrypted indexes to achieve efficient SVM classification. We define a leakage function, formulate a security definition, and provide a simulation-based security proof for${\sf SSVMC}$. The performance analysis demonstrates that${\sf SSVMC}$achieves linear computational complexity when an SVM classifier (a.k.a., the clinical decision model) is pre-trained. The simulations evaluate the impact of several parameters on time costs. The experimental evaluations show the performance differences between${\sf SSVMC}$and several existing schemes in terms of time costs, storage costs, communication costs, and precisions in a real-world clinical dataset, which demonstrate that${\sf SSVMC}$is computationally efficient with high decision accuracy.

In recent years, erasure codes have become the de facto standard for data protection in large scale distributed cloud storage systems at the cost of an affordable storage overhead. However, traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quiet different from all prior studies, in this paper, our erasure coding technique makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the system reliability and reconstruction performance. Specifically, our proposed technique, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffering from less repair cost than others, leading to a substantial improvement of the overall reliability and repair performance for cloud storage systems. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFP-LRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10 to 15 percent improvement in throughput, and an 8 to 12 percent reduction in decoding latency, while retaining a comparable overall reliability.

Secure search techniques over encrypted cloud data allow an authorized user to query data files of interest by submitting encrypted query keywords to the cloud server in a privacy-preserving manner. However, in practice, the returned query results may be incorrect or incomplete in the dishonest cloud environment. For example, the cloud server may intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide a query results verification mechanism that allows the data user to verify results. In this paper, we design a secure, easily integrated, and fine-grained query results verification mechanism, by which, given an encrypted query results set, the query user not only can verify the correctness of each data file in the set but also can further check how many or which qualified data files are not returned if the set is incomplete before decryption. The verification scheme is loose-coupling to concrete secure search techniques and can be very easily integrated into any secure query scheme. We achieve the goal by constructing secure verification object for encrypted cloud data. Furthermore, a short signature technique with extremely small storage cost is proposed to guarantee the authenticity of verification object and a verification object request technique is presented to allow the query user to securely obtain the desired verification object. Performance evaluation shows that the proposed schemes are practical and efficient.

Decision tree classification has become a prevailing technique for online diagnosis services. By outsourcing computation intensive tasks to a cloud server, cloud-assisted online diagnosis services are better ways for cases that the storage and computation requirements exceed the capability of medical institutions. With privacy concerns as well as intellectual property protection issues, the valuable diagnosis classifier and the sensitive user data should be protected against the cloud server. In this paper, we identify a work-flow for cloud-assisted online diagnosis services. We propose an efficient and secure decision tree classification scheme in the proposed work-flow. Specifically, the medical institution transforms a locally pre-trained decision tree classifier to a decision table, and later uses searchable symmetric encryption to encrypt the decision table. Then, the encrypted table is outsourced to the cloud server, and a user can submit encrypted physiological features to the cloud server and obtain an encrypted diagnosis prediction back. We provide formal security proofs to demonstrate that our scheme protects the confidentiality of the decision tree classifier and the user's data. The performance analysis shows that our scheme achieves faster-than-linear classification speed. Experimental evaluations show that our scheme requires several micro-seconds to process a diagnosis request in the tested datasets.

Cross correlations are ubiquitous in time-series IoT data sets such as trajectories from smartphones and smart meters data in smart grids. Conventional privacy methods have difficulty to protect cross correlation privacy within such correlated data set. Here we propose a novel Correlated noise mechanism for Cross-correlated Data Privacy (CCDP). Because the Fourier coefficients of the cross correlation of two data records are the linear product of those of the two data records, the sanitizing Fourier coefficients noise is used for efficient optimization. Also, the noise is added via the Geometric sum method, which is proved to provide the required Laplace distribution. We perform rigorous mathematical analysis of the CCDP and prove that it satisfies ε-Pufferfish privacy. We also prove that the CCDP can achieve the optimal data utility for a given privacy budget ε. What's more important, we further derive the mathematical procedure to obtain the optimal Laplace noise scale parameter to achieve better data utility. Simulations show that the proposed CCDP outperforms the independent Fourier coefficients noise mechanism, as well as two other state-of-the-art time-domain privacy mechanisms in the literature, for three types of data sets: computer-generated data, real-world trajectory data, and smart meter data.

The low-rate denial of service (LDoS) attack mainly exploits security vulnerabilities of adaptive mechanisms in network protocols and application services. The high-rate attack pulses within a short time interval are sent periodically, which will result in the degradation of service quality. The attack traffic is similar to normal traffic from legitimate users in the network, consequently, it is easy to escape the traditional detection methods because of its intermittence. Research works have demonstrated that there are fractal characteristics (self-similarity) of the network traffic over the large scale of time. Although the fractal characteristics of the network traffic will be changed under the LDoS attack, the variations of the fractal characteristics in some network states are not apparent in real-time detection. Based on the fractal characteristics, the fractal residual of the network traffic is analyzed through the Hurst parameters calculating process by R/S algorithm in this article. It can be found that the fractal residual of the network traffic can better reflect the different states of the LDoS attack. Combining the idea behind the sliding window, a novel fractal residual based real-time detection (FR-RED) method of the LDoS attack is proposed. The effectiveness of the method in this article is verified by performing some experiments on two platforms, the NS2 and test-bed. The results manifest the beginning and end of the LDoS attack can be estimated in real-time with high detection accuracy.

With the proliferation of cloud services, cloud-based image retrieval services enable large-scale image outsourcing and ubiquitous image searching. While enjoying the benefits of the cloud-based image retrieval services, critical privacy concerns may arise in such services since they may contain sensitive personal information. In this paper, we propose an efficient and Privacy-Preserving Image Retrieval scheme with Key Switching Technique (PPIRS). PPIRS utilizes the inner product encryption for measuring Euclidean distances between image feature vectors and query vectors in a privacy-preserving manner. Due to the high dimension of the image feature vectors and the large scale of the image databases, traditional secure Euclidean distance comparison methods provide insufficient search efficiency. To prune the search space of image retrieval, PPIRS tailors key switching technique (KST) for reducing the dimension of the encrypted image feature vectors and further achieves low communication overhead. Meanwhile, by introducing locality sensitive hashing (LSH), PPIRS builds efficient searchable indexes for image retrieval by organizing similar images into a bucket. Security analysis shows that the privacy of both outsourced images and queries are guaranteed. Extensive experiments on a real-world dataset demonstrate that PPIRS achieves efficient image retrieval in terms of computational cost.

In this article, we tackle the cross-modal video moment localization issue, namely, localizing the most relevant video moment in an untrimmed video given a sentence as the query. The majority of existing methods focus on generating video moment candidates with the help of multi-scale sliding window segmentation. They hence inevitably suffer from numerous candidates, which result in the less effective retrieval process. In addition, the spatial scene tracking is crucial for realizing the video moment localization process, but it is rarely considered in traditional techniques. To this end, we innovatively contribute a spatial-temporal reinforcement learning framework. Specifically, we first exploit a temporal-level reinforcement learning to dynamically adjust the boundary of localized video moment instead of the traditional window segmentation strategy, which is able to accelerate the localization process. Thereafter, a spatial-level reinforcement learning is proposed to track the scene on consecutive image frames, therefore filtering out less relevant information. Lastly, an alternative optimization strategy is proposed to jointly optimize the temporal- and spatial-level reinforcement learning. Thereinto, the two tasks of temporal boundary localization and spatial scene tracking are mutually reinforced. By experimenting on two real-world datasets, we demonstrate the effectiveness and rationality of our proposed solution.

Retrieving video moments from an untrimmed video given a natural language as the query is a challenging task in both academia and industry. Although much effort has been made to address this issue, traditional video moment ranking methods are unable to generate reasonable video moment candidates and video moment localization approaches are not applicable to large-scale retrieval scenario. How to combine ranking and localization into a unified framework to overcome their drawbacks and reinforce each other is rarely considered. Toward this end, we contribute a novel solution to thoroughly investigate the video moment retrieval issue under the adversarial learning paradigm. The key of our solution is to formulate the video moment retrieval task as an adversarial learning problem with two tightly connected components. Specifically, a reinforcement learning is employed as a generator to produce a set of possible video moments. Meanwhile, a pairwise ranking model is utilized as a discriminator to rank the generated video moments and the ground truth. Finally, the generator and the discriminator are mutually reinforced in the adversarial learning framework, which is able to jointly optimize the performance of both video moment ranking and video moment localization. Extensive experiments on two well-known datasets have well verified the effectiveness and rationality of our proposed solution.

Cloud-assisted Internet of Things (IoT) has become an increasingly popular technological trend as the performance of IoT applications can be greatly improved by delegating the cloud to manage massive IoT data. To protect the confidentiality of data outsourced from IoT devices to the cloud, cryptographic mechanisms are usually employed to encrypt the data in such a way that only the user designated by the data owner can decrypt the data. However, in the IoT multiuser environment, the encrypted data may also need to be shared with more users beyond the initially designated one. In this article, we propose a flexible privacy-preserving data sharing (FPDS) scheme in cloud-assisted IoT. With the FPDS scheme, an IoT user can encrypt data to a recipient by using identity-based encryption. More importantly, the IoT user can specify a fine-grained access policy to generate a delegation credential, and then send this credential to the cloud so that it can convert all the encrypted data satisfying the access policy into new ciphertexts that are readable to a new recipient. In this way, IoT users can share the data outsourced to the cloud in a flexible and privacy-preserving manner. Detailed security analysis shows that the FPDS scheme is secure against semitrusted cloud and malicious IoT users. Thorough theoretical and experimental analyses demonstrate the high efficiency of the scheme.

New privacy implications are induced to individuals and families because of the time-series data classification problem in the Internet of Things such as appliance classifications in the smart grid. To prevent the adversary from inferring the household appliance classification used in the smart grid, a singular spectrum analysis (SSA) has been applied to the local differential privacy (SSA-LDP). First, the Fourier spectrum noise has been added via the geometric sum which has been proved to achieve the Laplace noise distribution. Furthermore, we have proved that the sanitized data through the SSA-LDP is ε -deferentially private for the adversary inference attack. In addition, to achieve a better data utility, a formula has been obtained for the optimal Fourier spectrum noise by decomposing it into the superposition of power spectra of the dominant SSA eigenfilters. Finally, experiments have been performed with a computer-generated data set and a real-world smart-meter data set. Comparisons to other privacy approaches show that the optimized SSA-LDP does achieve a better data utility for a given data privacy.

In traditional steganographic schemes, RGB three channels payloads are assigned equally in a true color image. In fact, the security of color image steganography relates not only to data-embedding algorithms but also to different payload partition. How to exploit inter-channel correlations to allocate payload for performance enhancement is still an open issue in color image steganography. In this paper, a novel channel-dependent payload partition strategy based on amplifying channel modification probabilities is proposed, so as to adaptively assign the embedding capacity among RGB channels. The modification probabilities of three corresponding pixels in RGB channels are simultaneously increased, and thus the embedding impacts could be clustered, in order to improve the empirical steganographic security against the channel co-occurrences detection. The experimental results show that the new color image steganographic schemes, incorporated with the proposed strategy, can effectively make the embedding changes concentrated mainly in textured regions, and achieve better performance on resisting the modern color image steganalysis.

A mutual correlation between trajectories of two users is very helpful to real-life applications such as product recommendation and social media. While providing tremendous benefits, the releasing of correlated trajectories may leak sensitive social relations, due to potential links between mutual correlations and social relations. To the best of our knowledge, we take the first step to propose a mathematically rigorous n-body Laplace framework, satisfying "-differential privacy, which efficiently prevents a social relation inference through the mutual correlation between n-node trajectories of two users. The problem is mathematically formulated by defining a trajectory correlation score to measure the social relation between two users. Then, under the n-body Laplace framework, we propose two Lagrange Multiplier-based Differentially Private (LMDP) approaches to optimize the privacy budgets, for the data utility measured by location distances and the data utility measured by location correlations, i.e., UD-LMDP and UC-LMDP. Also, we present detailed analyses of privacy, data utility, adversary knowledge and the constrained optimizations. Finally, we perform experimental studies with real-life data. Our experimental results show that our proposed approaches achieve better privacy and data utility than the existing approaches.

With the rapid development of cloud computing, an increasing number of individuals and organizations are sharing data in the public cloud. To protect the privacy of data stored in the cloud, a data owner usually encrypts his data in such a way that certain designated data users can decrypt the data. This raises a serious problem when the encrypted data needs to be shared to more people beyond those initially designated by the data owner. To address this problem, we introduce and formalize an identity-based encryption transformation (IBET) model by seamlessly integrating two well-established encryption mechanisms, namely identity-based encryption (IBE) and identity-based broadcast encryption (IBBE). In IBET, data users are identified and authorized for data access based on their recognizable identities, which avoids complicated certificate management in usual secure distributed systems. More importantly, IBET provides a transformation mechanism that converts an IBE ciphertext into an IBBE ciphertext so that a new group of users not specified during the IBE encryption can access the underlying data. We design a concrete IBET scheme based on bilinear groups and prove its security against powerful attacks. Thorough theoretical and experimental analyses demonstrate the high efficiency and practicability of the proposed scheme.

Data classification has become an important and prevailing technique for big data analytics. Typically, a data classifier is designed and outsourced to a public cloud. A service provider then can easily provide various services and handle frequent and massive classification requests from users. With privacy concerns as well as Intellectual Property(IP) protection issues, the valuable classifier and the sensitive user data cannot be directly exposed to the public cloud. In this paper, we focus on the Support Vector Machine (SVM), one of the most popular classifiers, and propose an efficient and privacy-preserving outsourcing scheme for SVM classification in public clouds. Specifically, the service provider is allowed to transform the traditional SVM classifier to fixed hyper-rectangles and the order-preserving encryption is utilized to encrypt these hyper-rectangles as the encrypted classifier. Afterwards, the encrypted classifier is outsourced to the public cloud, and a user can submit an encrypted range query to the cloud and obtain the classification results back. Security analysis and extensive experimental evaluation demonstrate that our scheme can protect the confidentiality of classifier and users' data and achieves efficient SVM classification in terms of computational cost.

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms usually work off-line and cannot timely detect anomalies. They also suffer from high cost for storage and computation. Although online and accurate traffic anomaly detection is very important, it very difficult to achieve. We propose to utilize tensor model to well exploit the multi-dimensional information hidden in the traffic data for more accurate online Internet anomaly detection. We decouple the tensor recovery problem to iteratively solve two sub problems, a tensor factorization sub-problem and an anomaly detection sub-problem. To reduce the high cost for computation and storage involved in tensor factorization, we propose two lightweight techniques to effectively derive factor matrices of tensor in the current window and iteration, taking advantage of tensor decomposition results of the previous window and iteration. We have done extensive experiments using two real traffic traces to compare with three tensor based algorithms and three matrix based algorithms. The experiment results demonstrate that our online anomaly detection algorithm can achieve the same anomaly detection accuracy as that of the best offline tensor based algorithm, but at 6100 times faster speed and with very low storage cost.

Recently, code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP), have emerged as a new class of ingenious security threats. Attackers can utilize CRAs to hijack the control flow of programs to perform malicious actions without injecting any codes. Many defenses, classed into software-based and hardware-based, have been proposed. However, software-based methods are difficult to be deployed in practical systems due to high performance overhead. Hardware-based methods can reduce performance overhead but may require extending instruction set architectures (ISAs) and modifying the compiler or suffer the vulnerability of key leakage. To tackle these issues, this paper proposes a new hardware-assisted control flow checking method to resist CRAs with negligible performance overhead without extending ISAs, modifying the compiler or leaking the encryption/decryption key. The key technique involves two control flow checking mechanisms. The first one is the encrypted Hamming distances matching between the physical unclonable function (PUF) response and the return addresses, which prevents attackers from returning between gadgets so long as the PUF response is secret, thus resisting ROP attacks. The second one is the linear encryption/decryption operation (XOR) between the PUF response and the instructions at target addresses of call and jmp instructions to defeat JOP attacks. Advanced return-based full-function reuse attacks will be prevented with the dynamic key-updating method. Experimental evaluations on benchmarks demonstrate that the proposed method introduces negligible 0.95% runtime overhead and 0.78% binary size overhead on average.

Ensuring transmission survivability is a crucial problem for high-speed networks. Path protection is a fast and capacity-efficient approach for increasing the availability of end to end connections. The emerging SDN infrastructure makes it feasible to provide diversity routing in a practical network. For more robust path protection, it is desirable to provide an alternative path that does not share any risk resource with the active path. We consider finding the SRLG-Disjoint paths, where a Shared Risk Link Group (SRLG) is a group of network links that share a common physical resource whose failure will cause the failure of all links of the group. Since the traffic is carried on the active path most of time, it is useful that the weight of the shorter path of the disjoint path pair is minimized, and we call it Min-Min SRLG-Disjoint routing problem. The key issue faced by SRLG-Disjoint routing is the trap problem, where the SRLG-disjoint backup path (BP) can not be found after an active path (AP) is decided. Based on the min-cut of the graph, we design an efficient algorithm that can take advantage of existing search results to quickly look for the SRLG-Disjoint path pair. Our performance studies demonstrate that our algorithm can outperform other approaches with a higher routing performance while also at a much faster speed.

Malware detection has become mission sensitive as its threats spread from computer systems to Internet of things systems. Modern malware variants are generally equipped with sophisticated packers, which allow them bypass modern machine learning based detection systems. To detect packed malware variants, unpacking techniques and dynamic malware analysis are the two choices. However, unpacking techniques cannot always be useful since there exist some packers such as private packers which are hard to unpack. Although dynamic malware analysis can obtain the running behaviours of executables, the unpacking behaviours of packers add noisy information to the real behaviours of executables, which has a bad affect on accuracy. To overcome these challenges, in this paper, we propose a new method which first extracts a series of system calls which is sensitive to malicious behaviours, then use principal component analysis to extract features of these sensitive system calls, and finally adopt multi-layers neural networks to classify the features of malware variants and legitimate ones. Theoretical analysis and real-life experimental results show that our packed malware variants detection technique is comparable with the the state-of-art methods in terms of accuracy. Our approach can achieve more than 95.6\% of detection accuracy and 0.048 s of classification time cost.

Location-based services (LBSs) are increasingly popular in today’s society. People reveal their location information to LBS providers to obtain personalized services such as map directions, restaurant recommendations, and taxi reservations. Usually, LBS providers offer user privacy protection statement to assure users that their private location information would not be given away. However, many LBSs run on third-party cloud infrastructures. It is challenging to guarantee user location privacy against curious cloud operators while still permitting users to query their own location information data. In this paper, we propose an efficient privacy-preserving cloud-based LBS query scheme for the multiuser setting. We encrypt LBS data and LBS queries with a hybrid encryption mechanism, which can efficiently implement privacy-preserving search over encrypted LBS data and is very suitable for the multiuser setting with secure and effective user enrollment and user revocation. This paper contains security analysis and performance experiments to demonstrate the privacy-preserving properties and efficiency of our proposed scheme.

In recent years, erasure codes have become the de facto standard for data protection of large scale distributed cloud storage systems at the cost of an affordable storage overhead. While traditional erasure coding schemes, such as Reed-Solomon codes, suffer from high reconstruction cost and I/Os. The recent past has seen a plethora of efforts to optimize the tradeoff between the reconstruction cost, I/Os and storage overhead. Quietly different from all prior studies, in this paper, our erasure coding technology makes the first attempt to take advantage of the unequal failure rates across the disks/nodes to optimize the reconstruction performance and system reliability. Specifically, our proposed technology, the Unequal Failure Protection based Local Reconstruction Code (UFP-LRC) divides the data blocks into several unequal-sized groups with local parities, assigning the data blocks stored on more failure-prone disks/nodes into the smaller-sized group, so as to provide unequal failure protection for each group. In this way, by exploiting the nonuniform local parity degrees, the proposed UFP-LRC enables the data blocks that are stored on more failure-prone disks/nodes to tolerate a greater number of failures while suffer from less repair cost than others, leading to a substantial improvement of overall repair performance and reliability for cloud storage system. We perform numerical analysis and build a prototype storage system to verify our approach. The analytical results show that the UFPLRC technique gradually outperforms LRC along the increase of failure rate ratio. Also, extensive experiments show that, when compared to LRC, UFP-LRC is able to achieve a 10% to 13% improvement in throughput, and a 8% to 12% reduction in decoding latency, while retaining a comparable overall reliability.

Malware detection becomes mission critical as its threats spread from personal computers to industrial control systems. Modern malware generally equips with sophisticated anti-detection mechanisms such as code-morphism, which allows the malware to evolve into many variants and bypass traditional code feature based detection systems. In this paper, we propose to disassemble binary executables into opcodes sequences, and then convert the opcodes into images. By comparing the opcode images generated from binary targets with the opcode images generated from known malware sample codes, we can detect if the target binary executables contain variants of these known malwares. Theoretical analysis and real-life experiments results show that malware detection using visualized analysis is comparable in terms of accuracy, our approach can significantly improve 15\% of detection accuracy when the detection set contains a large quantity of binaries and the training set is small.

For fast packet classification, the de-facto industry standard is to use Ternary Content Addressable Memory (TCAM) chips where each chip stores one classifier rule and a given packet is checked against all such rules in parallel. In spite of the TCAM advantages, for a large number of rules, the TCAM deployment becomes expensive and the power consumption increases significantly. Therefore, it is desirable to reduce the number of TCAM rules while retaining the original classification semantics. In this work, we present efficient graph-based algorithms and data structures that allow us to capture the rule ordering relationships and iteratively compress the TCAM rules. Through extensive experiments, we show that our algorithm achieves 75% reduction of firewall rule sets on an average and even achieves an additional 24% compression on the output rule set of the state-of-the-art solutions.

In the big data era, with the rapid development of location-based applications, GPS enabled devices and big data institutions, location correlation privacy raises more and more people's concern. Because adversaries may combine location correlations with their background knowledge to guess users' privacy, such correlation should be protected to preserve users' privacy. In order to deal with the location disclosure problem, location perturbation and generalization have been proposed. However, most proposed approaches depend on syntactic privacy models without rigorous privacy guarantee. Furthermore, many approaches only consider perturbing the locations of one user without considering multi-user location correlations, so these techniques cannot prevent various inference attacks well. Currently, differential privacy has been regarded as a standard for privacy protection, but there are new challenges for applying differential privacy in the location correlations protection. The privacy protection not only should meet the needs of users who request location-based services, but also should protect location correlation among multiple users. In this paper, we propose a systematic solution to protect location correlations privacy among multiple users with rigorous privacy guarantee. First of all, we propose a novel definition, private candidate sets which are obtained by hidden Markov models. Then, we quantify the location correlation between two users by using the similarity of hidden Markov models. Finally, we present a private trajectory releasing mechanism which can preserve the location correlations among users who move under hidden Markov models in a period of time. Experiments on real-world datasets also show that multi-user location correlation protection is efficient.

Recently, secure search over encrypted cloud data has become a hot research spot and challenging task. Some secure search schemes have been proposed to try to meet this challenge. In this paper, we propose a conjunctive multi-keyword secure search scheme for multiple data owners. To guarantee data security and system flexibility in the multiple data owners environment, we design an ingenious secure query scheme that allows each data owner to adopt randomly chosen temporary keys to build secure indexes for different data files. An authorized data user does not need to know these temporary keys of constructing indexes and can instead randomly choose another temporary query keys to encrypt query keywords while the cloud can correctly perform keywords matching over encrypted data files. Extensive experiments demonstrate the correctness and practicality of the proposed scheme.

Malware detection becomes mission critical as its threats spread from personal computers to industrial control systems. Modern malware generally equips with sophisticated anti-detection mechanisms such as code-morphism, which allows the malware to evolve into many variants and bypass traditional code feature based detection systems. In this paper, we propose to disassemble binary executables into opcodes sequences, and then convert the opcodes into images. By using convolutional neural network to compare the opcode images generated from binary targets with the opcode images generated from known malware sample codes, we can detect if the target binary executables is malicious. Theoretical analysis and real-life experiments results show that malware detection using visualized analysis is comparable in terms of accuracy, our approach can significantly improve 15% of detection accuracy when the detection set contains a large quantity of binaries and the training set is much smaller.

Software-Defined Networks (SDN) is a trend of research in networks. Rule placement, a common operation for network administrators, has become more complicated due to the capacity limitation of devices in which the large number of rules are deployed. Prior works on rule placement mostly consider the influence on rule placement incurred by the rules in a single device. However, the position relationships between neighbor devices have influences on rule placement. Our basic idea is to classify the position relationships into two categories: the serial relationship and the parallel relationship, and we present a novel strategy for rule placement based on the two different position relationships. There are two challenges of implementing our strategies: to check whether a rule is contained by a rule set or not and to check whether a rule can be merged by other rules or not.To overcome the challenges, we propose a novel data structure called OPTree to represent the rules, which is convenient to check whether a rule is covered by other rules. We design the insertion algorithm and search algorithm for OPTree. Extensive experiments show that our approach can effectively reduce the number of rules while ensuring placed rules work. On the other hand, the experimental results also demonstrate that it is necessary to consider the position relationships between neighbor devices when placing rules.

Leveraging a wireless multi-path channel as a source of common randomness, a number of key generation methods have been proposed according to information-theory security. However, by taking the advantages of node's mobility, existing schemes usually have low generation rate or low entropy. To overcome this limitation, we present a key generation protocol with known Artificial Interference, named Smoke Grenade, a new physical-layer approach for secret key generations in a narrowband fading channel. Our scheme utilizes artificial interference to contribute to the change of the measured values on channel states. The theoretical analysis shows that the key generation rate rises with the increment of the interference power. Particularly, the achievable key rate of Smoke Grenade achieves at least four times better than that of traditional key generation schemes when the average interference power is normalized to 1. Simulation results also show that Smoke Grenade has a higher generation rate and entropy compared with some known state-of-the-art approaches.

Leveraging a wireless multipath channel as the source of common randomness, many key generation methods have been proposed according to the information-theory security. However, existing schemes suffer a low generation rate and a low entropy, and mainly rely on nodes' mobility. To overcome this limitation, we present a key generation protocol with known artificial interference, named SmokeGrenade, a new physical-layer approach for secret key generation in a narrowband fading channel. Our scheme utilizes artificial interference to contribute to the change of measured values on channel states. Our theoretical analysis shows that the key generation rate increases with the increment of the interference power. Particularly, the achievable key rate of SmokeGrenade gains three times better than that of the traditional key generation schemes when the average interference power is normalized to 1. Simulation results also demonstrate that SmokeGrenade achieves a higher generation rate and entropy compared with some state-of-the-art approaches.

We describe the design and implementation of WINDAR, an object-oriented toolkit for transparent rollback-recovery of distributed applications running on Windows platform. In WINDAR, the workloads of a process are multithreaded, exploiting effectively processor execution resources to improve execution efficiency. In addition, WINDAR's unified framework for various rollback recovery protocols enables dynamic protocol configuration to adapt itself to the need of recovery-oriented computing (ROC) and distributed computations in Internet environment. WINDAR was evaluated using three benchmarks. It is observed that multithreading is an effective approach to improve the performance of message logging protocols, especially for pessimistic message logging. In our experiment, the overhead ratio of pessimistic message logging was reduced to the same magnitude as that of the optimistic message logging for three benchmarks.