Deep neural networks are increasingly vulnerable to physically deployable backdoor attacks, which manipulate real-world objects to induce targeted model failures. However, current physical backdoor attacks predominantly rely on perpetually visible triggers appended to target objects. These methods inevitably expose attack traces during the deployment phase, risking human suspicion prior to activation. In this paper, we propose a conditionally-visible physical backdoor attack, which can only be activated under specific optical conditions and thereby overcomes the risk of being detected after deployment and before the attack. Specifically, to ensure robust and reliable activation, we design irregular polygonal pattern as triggers to against across environmental variations. Moreover, we introduce a dual-phase mechanism (dormant and activated) to enable stealthy deployment. Our trigger remains invisible and dormant under non-attack conditions, leaving no physical traces. It activates instantaneously under specific illumination, inducing the target model to perform the desired behavior. We conduct experiments on traffic sign recognition tasks to compare our attack with six digital and seven physical attacks, and assess its performance against potential defenses. Extensive experimental results demonstrate the effectiveness, stealthiness, and robustness of our attack.

The vast number of devices and the complexity of requirements present significant challenges in ensuring the security of Non-Terrestrial Internet of Things (NT-IoT). Although existing studies have proposed methods like to defend against data theft and network interference attacks, there is still a need for more in-depth research on detecting data-level attacks in NTNs. Moreover, the vast and diverse nature of network traffic presents significant challenges in traffic modeling and feature extraction. Hypergraph neural networks have gained considerable attention because of capabilities in data modeling and feature extraction. However, most existing hypergraph neural networks are tailored for specific applications and are not adaptable to the detection of malicious encrypted traffic. To address these challenges, we firstly propose a hypergraph neural network-based malicious encrypted traffic detection framework to enhance the resilience of NT-IoT, enabling attack detection across unmanned aerial vehicles, base stations and satellites. Then, we introduce a Multidimensional Encrypted Traffic HyperGraph Network (METHGN). METHGN models the encrypted traffic from network, connection and time dimensions using hypergraph and uses hypergraph convolution network to extracts and fuse features. We conducted comparative experiments on IoT and The Onion Router Network encrypted traffic datasets for different classification tasks. Extensive experiments demonstrate the effectiveness and superiority of our approach.

Recent studies show that deep learning-based traffic sign recognition systems are vulnerable to backdoor attacks. These compromised models can be activated to misclassify traffic signs when exposed to specific backdoor patterns during inference. Nevertheless, existing attack methods rely on exogenous triggers (e.g., stickers or patches) that introduce external features to associate backdoor patterns with target labels, significantly increasing attack complexity. In this paper, we propose FlipBAT, a stealthy endogenous backdoor attack method that uses the image’s self-flipping as the built-in trigger, eliminating the need for external trigger patterns. Our attack supports two distinct attack modes: a multi-class backdoor attack that enables flexible target diversification via cyclic mappings, and a single-class backdoor attack that achieves higher stealthiness by minimally perturbing the source class. Extensive experiments conducted on two standard traffic sign recognition datasets (GTSRB and BelgiumTS) across three different victim models demonstrate that FlipBAT effectively establishes robust mappings between backdoor images and target classes. Notably, our method achieves efficient backdoor attacks with significantly lower poisoning rates compared to conventional approaches. Our method has also been shown to be robust against state-of-the-art backdoor defenses.

Uncrewed aerial vehicle (UAV) networks are increasingly exposed to widespread and various network attacks due to their fully distributed nature and the limited defensive capabilities of individual devices. Existing defense strategies rely on network connectivity and UAV status information, which overlook information of network traffic. Malicious traffic detection offers a promising solution to achieve fine-grained attack detection. However, the dynamic nature and complexity of UAV networks limit the effectiveness of traditional traffic detection methods. Current approaches either fail to fully exploit the raw characteristics of traffic or do not consider the timeliness requirements of UAV networks. To address these challenges, we propose a novel twin graph encoder neural network, which can extract features of raw traffic bytes for efficient traffic detection. First, we propose a decoupled architecture for model training and inference to enable efficient detection of malicious traffic in UAV networks. Second, we propose a novel modeling method that models traffic as the co-occurrence graph and word frequency graph based on raw bytes. Then, we propose TGE-ETD, a Twin Graph Encoder for Encrypted Traffic Detection. TGE-ETD consists of a set of twin graph encoders that effectively extract intrinsic traffic features from graphs constructed from raw bytes. In addition, TGE-ETD employs a global attention pooling mechanism to effectively distinguish the feature contributions of different bytes. Finally, we conducted extensive experiments on a real UAV traffic dataset and four real-world network traffic datasets. TGE-ETD achieved an improvement of 1%-20% over the baseline methods by reducing the number of parameters by 20 times. Tested on multiple UAV hardware devices, TGE-ETD can achieve millisecond-level traffic detection.

The large number of devices and complex communication requirements pose challenges to ensuring the security of Non-Terrestrial Internet of Things (NT-IoT). The large-scale data and complex communication requirements make accurate detection of malicious traffic even more challenging in NT-IoT. Hypergraph neural networks have strong performance in extracting multi-relational features. However, most existing hypergraph neural networks are tailored for graph data, and hyperedge construction methods are not well-suited. To address these challenges, we propose a malicious encrypted traffic detection method based on a hypergraph neural network. First, we propose an efficient hypergraph construction method for encrypted traffic named JointKNN. JointKNN calculates the Euclidean distance between traffic flows and adds the target nodes into the neighbor sets to form the hyperedges. Then, we propose an Encrypted Traffic HyperGraph Convolution Network (ETHGCN), which takes the encrypted traffic hypergraph as the input. ETHGCN extracts and fuses both connection and temporal features to accurately detect malicious traffic. We conduct comparative experiments on IoT and Onion Network encrypted traffic datasets for multi-class and binary classification tasks. Results indicate that ETHGCN achieves an accuracy exceeding 99.8% in IoT tasks and demonstrates an improvement of nearly 20% in Onion Network tasks.

Vertical federated learning (VFL) shows promise for enabling collaborative learning among Internet of Vehicle systems (IoVs) without requiring the sharing of private training data. However, existing work has exposed VFL’s vulnerability to privacy-stealing attacks, where an honest but curious server might reconstruct a client’s raw data from client-uploaded embeddings. In this work, we first elucidate the intrinsic mechanisms of privacy attacks from an information theory perspective, which provides a solid foundation for potential defensive strategies. Based on our findings, we introduce PriVFL, a defense mechanism based on information bottleneck theory. PriVFL is designed to safeguard the privacy of VFL-based IoVs by enabling shared embeddings to extract minimal information from input data, while preserving the information essential to target labels. Specifically, PriVFL restricts the information contained in embeddings by reducing the upper bound of mutual information between the raw samples and embeddings uploaded from local clients. Meanwhile, PriVFL ensures the effectiveness of the model by increasing the mutual information lower bound between embeddings and samples’ labels. Our evaluation includes 5 benchmark data sets and 4 different models. Experimental results demonstrate that PriVFL effectively mitigates privacy attacks while preserving the model’s effectiveness. These findings underscore that PriVFL can significantly enhance the privacy of VFL-based IoVs, thereby bolstering the development of practical IoV applications.

Ensuring fairness in incentive mechanisms for federated learning (FL) is essential to attracting high-quality clients and building a sustainable FL ecosystem. Most existing fairness-aware incentive mechanisms distribute rewards to FL clients by quantifying their contributions to the performance of the global model. Essentially, these mechanisms pursuecontribution fairness, namely a constant contribution-reward ratio across FL clients, with an implicit assumption that clients would be satisfied with thecontribution fairness. However, research in social psychology has confirmed that this assumption may not hold in many real-world scenarios. According to equity theory proposed by Adams, an individual’s assessment and perception of receiving fair treatment significantly depend on the input-outcome ratio, where outcome simply refers to the rewards, while input is far more complex because it involves a bunch of subtle factors such as enthusiasm, experience and tolerance as well as the estimated contributions. Inspired by Adams’ equity theory, in this work, we expand the notion ofcontribution fairnesstoinput fairnessand propose a new fairness-aware incentive mechanism namedFairRewardthat distributes rewards under the joint consideration of self-reported inputs and computed contributions.FairRewardemploys a reputation mechanism to enhance the credibility of self-reported inputs and leverages blockchains to eliminate the need of a trusted FL server and monetarily incentivize/penalize clients. In addition,FairRewardadopts techniques including distributed differential privacy and locality-sensitive hashing to address privacy and non-IID issues in FL. Moreover, we conduct a comprehensive security and privacy analysis. Finally, we evaluateFairRewardthrough extensive experiments. The comprehensive experimental results demonstrate thatFairRewardis effective, scalable and attack-resistant, and provides theinput fairnessrequired.

Vertical Federated Learning (VFL) is a collaborative learning paradigm where participants share the same sample space while splitting the feature space. In VFL, local participants host their bottom models for feature extraction and collaboratively train a classifier by exchanging intermediate results with the server owning the labels. Both local training data and bottom models contain privacy-sensitive information and are considered the intellectual property of each participant, and thus should be protected by the design of VFL. Our study exposes the fundamental susceptibility of VFL systems to privacy leaks, which arise from the collaboration between the server and clients during both training and testing. Based on our findings, we proposePISTE, a model-agnostic framework of privacy stealing attacks against VFL. PISTE delivers three privacy inference attacks, i.e., model stealing, data reconstruction, and property inference attacks on five benchmark datasets and four different model architectures. We further discuss four potential countermeasures. Experimental results show that all of them cannot prevent all three privacy stealing attacks in PISTE. In summary, our study demonstrates the inherent yet rarely uncovered vulnerability of VFL on leaking data and model privacy.

Vertical Federated Learning (VFL) is susceptible to various one-party hijacking attacks, such as Replay and Generation attacks, where a single malicious client can manipulate the model to produce attacker-specified results, thereby compromising its reliability in real-world deployments. In this paper, we first uncover the underlying mechanisms of these attacks and observe that successful attacks induce significant discrepancies in the embedding-label associations across different clients. We establish a theoretical framework demonstrating how these discrepancies can serve as reliable indicators for detecting hijacking attempts. Building upon this insight, we propose VFLMonitor, a robust defense mechanism that leverages these embedding-label discrepancies to detect and mitigate hijacking attacks. Specifically, VFLMonitor identifies suspicious queries by analyzing differences in label estimations from multiple clients and applies a majority voting rule to correct or filter out these malicious queries. Moreover, VFLMonitor introduces a novel regularization strategy during training to reduce intra-class variance in embeddings, thereby enhancing their discriminative power and improving defense effectiveness. Extensive experi21 ments were conducted on 5 real-world datasets against 2 different attack types under 3 attack scenarios. The results demonstrate that VFLMonitor can effectively identify and exclude potential hijacked requests in all types of one-party hijacking attacks, while maintaining a meager false positive rate for legitimate queries.

Intelligent traffic signal systems, crucial for intelligent transportation systems, have been widely studied and deployed to enhance vehicle traffic efficiency and reduce air pollution. Unfortunately, intelligent traffic signal systems are at risk of data spoofing attack, causing traffic delays, congestion, and even paralysis. In this paper, we reveal a multivehicle collaborative data spoofing attack to intelligent traffic signal systems and propose a collaborative attack sequence generation model based on multiagent reinforcement learning (RL), aiming to explore efficient and stealthy attacks. Specifically, we first model the spoofing attack based on Partially Observable Markov Decision Process (POMDP) at single and multiple intersections. This involves constructing the state space, action space, and defining a reward function for the attack. Then, based on the attack modeling, we propose an automated approach for generating collaborative attack sequences using the Multi‐Actor‐Attention‐Critic (MAAC) algorithm, a mainstream multiagent RL algorithm. Experiments conducted on the multimodal traffic simulation (VISSIM) platform demonstrate a 15% increase in delay time (DT) and a 40% reduction in attack ratio (AR) compared to the single‐vehicle attack, confirming the effectiveness and stealthiness of our collaborative attack.

Graph neural networks, effectively harnessing the extensive interactive data from Internet of Things (IoT) devices, significantly enhance service quality in IoT systems. However, traditional centralized training leads to the leakage of private data during the data collection and model training phases in IoT scenarios. Federated learning (FL) has emerged as a promising approach, facilitating collaborative model training across diverse IoT devices without sharing sensitive data. The intricate types and relationships among IoT devices from various institutions highlight the issues of class imbalance and graph heterogeneity across different clients. These issues decrease the performance of FL models. In this work, we focus on a more realistic scenario where the IoT institutions have only limited amount and types of data. We propose a cross-institutional federated heterogeneous graph learning method called FedHGL. It aims to mitigate the negative effects of class imbalance while maintaining the private data locally on clients during collaborative training. We employ a heterogeneous graph neural network as the training model for clients. FedHGL generates cross-client minority class samples to enhance the model performance. Additionally, it incorporates a compensation mechanism to prevent forgetting global information. FedHGL designs an adaptive aggregation coefficient that assigns weights to IoT institutions according to the class imbalance of their data, thereby optimizing the aggregation process. Extensive experiments demonstrate the effectiveness of FedHGL for class imbalance and heterogeneous graph data.

Dynamic link prediction aims to predict future connections among unconnected nodes in a network. It can be applied for friend recommendations, link completion, and other tasks. Network representation learning algorithms have demonstrated considerable effectiveness in various prediction tasks. However, most network representation learning algorithms are based on homogeneous networks and static networks for link prediction that do not consider rich semantic and dynamic information. Additionally, existing dynamic network representation learning methods neglect the neighborhood interaction structure of the node. In this work, we design a neighbor-enhanced dynamic heterogeneous attributed network embedding method (NeiDyHNE) for link prediction. In light of the impressive achievements of the heuristic methods, we learn the information of common neighbors and neighbors’ interaction in heterogeneous networks to preserve the neighbors proximity and common neighbors proximity. NeiDyHNE encodes the attributes and neighborhood structure of nodes as well as the evolutionary features of the dynamic network. More specifically, NeiDyHNE consists of the hierarchical structure attention module and the convolutional temporal attention module. The hierarchical structure attention module captures the rich features and semantic structure of nodes. The convolutional temporal attention module captures the evolutionary features of the network over time in dynamic heterogeneous networks. We evaluate our method and various baseline methods on the dynamic link prediction task. Experimental results demonstrate that our method is superior to baseline methods in terms of accuracy.

The Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) integrated architecture is a key enabling platform for 5G to run multiple customized services in the form of service function chain (SFC) configured as an ordered set of service functions (SFs). However, memory-related software aging in the SF that can be exploited by attackers becomes a new threat to the dependability of MEC-SFC services. To provide dependable MEC-SFC services, proactive rejuvenation techniques to counteract the SF aging problem are essential. In this paper, we develop a semi-Markov model to quantitatively investigate the transient availability and steady-state dependability (availability and reliability) of MEC-SFC services. Our model enables the analysis of a MEC-SFC with any number of SFs, and can capture complex time-dependent behaviors of aging, failure, and recovery. The approximate accuracies of the presented model on dependability measures are comprehensively evaluated through comparative studies with simulation experiments. We then detect potential bottlenecks for a MEC-SFC system through sensitivity analysis and further analyze the impact of event-time interval distributions on steady-state dependability. Finally, we investigate the transient behaviors of a MEC-SFC service when varying system parameters during MEC-SFC operation.

Data reconstruction attack has become an emerging privacy threat to Federal Learning (FL), inspiring a rethinking of FL's ability to protect privacy. While existing data reconstruction attacks have shown some effective performance, prior arts rely on different strong assumptions to guide the reconstruction process. In this work, we propose a novel Conditional Generative Instance Reconstruction Attack (CGIR attack) that drops all these assumptions. Specifically, we propose a batch label inference attack in non-IID FL scenarios, where multiple images can share the same labels. Based on the inferred labels, we conduct a “coarse-to-fine” image reconstruction process that provides a stable and effective data reconstruction. In addition, we equip the generator with a label condition restriction so that the contents and the labels of the reconstructed images are consistent. Our extensive evaluation results on two model architectures and five image datasets show that without the auxiliary assumptions, the CGIR attack outperforms the prior arts, even for complex datasets, deep models, and large batch sizes. Furthermore, we evaluate several existing defense methods. The experimental results suggest that pruning gradients can be used as a strategy to mitigate privacy risks in FL if a model tolerates a slight accuracy loss.

Maritime ships and ports have become increasingly digital and intelligent. While intelligent maritime transportation systems bring convenience to the maritime industry, ship operation and management are also confronted with network risks. The Internet of Things (IoT) installed in the shipborne network collects and monitors the environmental data of the whole ship. It uses the collected data to make decisions to control the ship. The threat of Local Area Network (LAN) of IoT in ships has become an emerging issue. The DNS rebinding attack is a typical attack, which can bypass firewalls and seriously threaten the marine network in security and privacy of the local IoT. DNS rebinding attacks are difficult to model and detect, due to their sophisticated characteristics. In this work, we define threat models of DNS rebinding attacks and propose an effective method for the detection of and the defense against these attacks. First, we define threat models for DNS rebinding attacks. We employ a Markov chain to model the process of DNS rebinding attacks. With the threat modeling, the attack behaviors are clearly characterized and the most relevant attributes are thus extracted. Second, we propose an effective method for the detection of DNS rebinding attacks in the marine transportation system. The detection method includes the initialization method and the verification method, which manages and verifies access permission of equipment information and the service interface of the IoT in the shipborn network. Finally, we simulate the DNS rebinding attacks on the marine IoT. We analyze and test the security and the performance of the initialization method and the verification method in the simulated environment. The extensive experimental results demonstrate that the IoT in marine networks is vulnerable to DNS rebinding. Our method is effective and efficient to detect and defend against DNS rebinding attacks. It thus secures security and privacy in the local IoT on shipboard.

Frequency hopping (FH) technology is one of the most effective technologies in the field of radio countermeasures, meanwhile, the recognition of FH signal has become a research hotspot. FH signal is a typical non-stationary signal whose frequency varies nonlinearly with time and the time-frequency analysis technique provides a very effective method for processing this kind of signal. With the renaissance of deep learning, methods based on time-frequency analysis and deep learning are widely studied. Although these methods have achieved good results, the recognition accuracy still needs to be improved. Through the observation of the datasets, we found that there are still difficult samples that are difficult to identify. Through further analysis, we propose a horizontal spatial attention (HSA) block, which can generate spatial weight vector according to the signal distribution, and then readjust the feature map. The HSA block is a plug-and-play module that can be integrated into common convolutional neural network (CNN) to further improve their performance and these networks with HSA block are collectively called HANets. The HSA block also has the advantages of high recognition accuracy (especially under low SNRs), easy to implant, and almost no influence on the number of parameters. We verified our method on two datasets and a series of comparative experiments show that the proposed method achieves good results on FH datasets.

Usage-based insurance (UBI) provides reasonable vehicle insurance premiums based on vehicle usage and driving behavior. In general, there are three major issues in realizing intelligent UBI systems. First, UBI evaluation mechanisms are not auditable to drivers. Insurers may thus deliberately adjust the UBI premiums. Second, the process of collecting driving data by insurers may lead to serious privacy breaches. Third, forging safer driving data for reducing insurance premiums may cause economic losses for insurers. To address these challenges, in this study, we propose CCUBI, a cross-chain-based premium competition scheme with privacy preservation for intelligent UBI systems. We introduce tamper-resistant blockchain and smart contracts to construct credible insurance mechanisms. The cross-chain technology connects these blockchains in the entire network to form an open premium competition scheme. Vehicle owners can assess designated insurers by sharing historical data with them to get a suitable CCUBI plan. In addition, we propose a data aggregation method used for CCUBI analysis with privacy preservation. Vehicle owners only publish proofs of the driving data. Proofs can still maintain privacy and computability in cross-chain flows. Finally, we adopt roadside units to detect forged driving data. We conduct a detailed security analysis. Experimental results also demonstrate the efficiency of CCUBI.

With the widespread application of frequency hop-ping (FH) technology, a large number of FH signal monitoring data have been accumulated. Big data brings new opportunities and challenges to radio supervision, one of which is signal retrieval. The task of signal retrieval is to find similar signals for a given segment of signal. In this paper, we propose an idea of FH signal retrieval. Firstly, transform the FH signal into two-dimensional images, and the radio signal retrieval problem is transformed into an image retrieval problem. Then, the advanced achievements in the field of image retrieval can be used to complete signal retrieval. Based on this idea, we propose an FH signal retrieval algorithm named FHSR. In order to extract the signal information better, we also propose a data augmentation algorithm. Experiments show that our method achieves good results in retrieval accuracy and speed, which meets the actual needs.

Moving target defense (MTD), emerging as a game-changer in the cyber defense area, has got a lot of attention and development recently. As a proactive defense technique, MTD dynamically changes system attributes in order to create more uncertainties of the system and has been proved to be effective against cyber attacks. Beyond this, there is still a lack of researches with respect to the quantitative analysis of the effect of MTD on system performance. This paper aims to quantitatively investigate how MTD affects system performance while bringing security. We develop Markov process-based models for two different MTD strategies and derive the formulas for metrics of interest. We carry out simulation experiments to validate our proposed models with Mininet. Furthermore, numerical analysis is conducted for comparing these two different strategies in terms of system performance. The numerical results also show how different parameters affect the evaluation metrics. Our models can help defenders conFigure the MTD system in the most suitable way.

Social networks have integrated into the daily lives of most people in the way of interactions and of lifestyles. The users' identity, relationships, or other characteristics can be explored from the social networking data, in order to provide personalized services to the users. In this article, we focus on predicting the user's emotional intelligence (EI) based on social networking data. As an essential facet of users' psychological characteristics, EI plays an important role on well-being, interpersonal relationships, and overall success in people's life. Perception of EI contributes to predicting one's behavior or group behavior. Most existing work on predicting people's EI is based on questionnaires that may collect dishonest answers or unconscientious responses, thus leading in potentially inaccurate prediction results. In this article, we are motivated to propose EI prediction models based on the sentiment analysis of social networking data. The models are represented by four dimensions, including self-awareness, self-regulation, self-motivation, and social relationships. The EI of a user is then measured by four numerical values or the sum of them. In the experiments, we predict the EIs of over a hundred thousand users based on one of the largest social networks of China, Weibo. The predicting results demonstrate the effectiveness of our models. The results show that the distribution of the four EI's dimensions of users is roughly normal. The results also indicate that EI scores of females are generally higher than males' EI scores. This is consistent with previous findings. In addition, the four dimensions of EI are correlated. We finally analyze the advantages and the disadvantages of our models in predicting users' EI with social networking data.

With the development of emerging intelligent traffic signal (I-SIG) system, congestion-involved security issues are drawing attentions of researchers and developers on the vulnerability introduced by connected vehicle technology, which empowers vehicles to communicate with the surrounding environment such as road-side infrastructure and traffic control units. A congestion attack to the controlled optimization of phases algorithm (COP) of I-SIG is recently revealed. Unfortunately, such analysis still lacks a timely visualized prediction on later congestion when launching an initial attack. In this paper, we argue that traffic image feature-based learning has available knowledge to reflect the relation between attack and caused congestion and propose a novel analysis framework based on cycle generative adversarial network (CycleGAN). Based on phase order, we first extract four-direction road images of one intersection and perform phase-based composition for generating new sample image of training. We then design a weighted L1 regularization loss that considers both last-vehicle attack and first-vehicle attack, to improve the training of CycleGAN with two generators and two discriminators. Experiments on simulated traffic flow data from VISSIM platform show the effectiveness of our approach.

Model-based performance evaluation of large-scale PaaS Fog Datacenter requires to develop a hierarchical model, which is usually composed of a series of monolithic models. This paper proposes an approximate analytic modeling approach to evaluate the performance of a pool of active containers on a PaaS Fog physical node under batch task arrivals, by using an $M^{[\mathrm{x}]}/G/m/m+K$ queue. We describe the details of the proposed model and the formulas for calculating performance measures of interest. Experiment results indicate that the proposed approach (including the model and the formulas) can approximately capture the system behaviors even when the task service-time distribution has a large coefficient of variation (>1.5).

Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, which has attracted a concern that is more critical than ever. However, recent studies discover that the interesting attack mode adversarial attack also be effective when targeting neural network policies in the context of reinforcement learning, which has inspired innovative researches in this direction. Hence, in this paper, we give the very first attempt to conduct a comprehensive survey on adversarial attacks in reinforcement learning under AI security. Moreover, we give briefly introduction on the most representative defense technologies against existing adversarial attacks.

Analyzing the survivability of a cloud service is critical as the application or service migration from local to cloud is an irresistible trend. However, former research on cloud service or virtual system (VS) availability and/or reliability was only carried out from the perspective of steady state. This paper aims to analyze the survivability of the cloud service after a service breakdown occurrence by presenting a model and the closed-form solutions with the use of continuous-time Markov chain. The service breakdown may be caused by virtual machine (VM) and/or VM monitor (VMM) bugs or software rejuvenation and/or host failures and NAS (Network Area Storage) failures. In order to improve the cloud service survivability, the VS applies two techniques: VM failover and VM live-migration. Through the model proposed and the survivability metrics defined in this paper, we are able to quantitatively assess the system survivability while providing insights into the investment efforts in system recovery strategies. In order to study the impact of key parameters on system survivability, this paper also provides a parameter sensitivity analysis through numerical experiments.

Summary Infrastructure as a Service cloud provides elasticity and scalable virtual machines (VMs) as computing service to multiple tenants, but the tenants lose the full control of their data. Measuring the integrity of critical files of the VMs and providing the integrity attestation to the tenants on the basis of TCG trusted computing techniques is an effective way to alleviate their anxiety. This paper considers how to measure the integrity of the processes run in guest VMs and files opened in guest VMs. We propose an out‐of‐the‐box integrity measurement approach to measure the integrity of critical files through system call (syscall) interception without any modification of the guest VMs. Out‐of‐the‐box integrity measurement approach can not only measure the integrity of all files that have been considered by existing approaches but also measure the integrity of the system configuration files, program loaders, and script interpreters, which affect the system behaviors and integrity. The ability of supporting both system and manual measurement policies makes our approach flexible. We implement this approach in Xen hypervisor with little modification of the existing syscall interception method, and this approach can be ported to other virtualization platform easily. Copyright © 2014 John Wiley & Sons, Ltd.

Android has been a major target of malicious applications (malapps). How to detect and keep the malapps out of the app markets is an ongoing challenge. One of the central design points of Android security mechanism is permission control that restricts the access of apps to core facilities of devices. However, it imparts a significant responsibility to the app developers with regard to accurately specifying the requested permissions and to the users with regard to fully understanding the risk of granting certain combinations of permissions. Android permissions requested by an app depict the app's behavioral patterns. In order to help understanding Android permissions, in this paper, we explore the permission-induced risk in Android apps on three levels in a systematic manner. First, we thoroughly analyze the risk of an individual permission and the risk of a group of collaborative permissions. We employ three feature ranking methods, namely, mutual information, correlation coefficient, and T-test to rank Android individual permissions with respect to their risk. We then use sequential forward selection as well as principal component analysis to identify risky permission subsets. Second, we evaluate the usefulness of risky permissions for malapp detection with support vector machine, decision trees, as well as random forest. Third, we in depth analyze the detection results and discuss the feasibility as well as the limitations of malapp detection based on permission requests. We evaluate our methods on a very large official app set consisting of 310 926 benign apps and 4868 real-world malapps and on a third-party app sets. The empirical results show that our malapp detectors built on risky permissions give satisfied performance (a detection rate as 94.62% with a false positive rate as 0.6%), catch the malapps' essential patterns on violating permission access regulations, and are universally applicable to unknown malapps (detection rate as 74.03%).

Interrupt coalescing (IC) technique has been used in general-purpose operating systems to mitigate receive livelock (RL) problem in gigabit Ethernet network hosts. Schemes for dynamically tuning the interrupt coalescing behavior of a communication interface based on traffic load or system state have been proposed. However, all the existing IC schemes are designed using heuristics. In this paper we present an analytical model for the IC technique and carry out a detailed study of existing IC schemes in terms of their performance characteristics including system goodput, CPU consumption and latency. We validate our analysis through measurement-based experiments.

Remote attestation is an important attribute in trusted computing. One of the purpose of remote attestation is to attest the remote platform is trusty but not revealing the actual identity of the platform. Direct anonymous attestation (DAA) is a kind of scheme which is adopted by Trusted Computing Group in the specification 1.2 to hide the privacy of the platform. But DAA involves various of zero-knowledge proofs and is not efficient to implement. To guarantee the trustworthiness and privacy, we propose a remote anonymous attestation protocol based on ring signature in this paper. We also show that our protocol is secure under the RSA assumption in random oracle model. Furthermore, the attestation protocol does not need the third party and extra zero-knowledge proof, which makes it very efficient in realization.