As a promising subdomain of intelligent transportation systems (ITS), Internet of Vehicles (IoV) can be empowered by digital twin (DT) technology for real-time traffic simulation and artificial intelligence (AI)-driven predictive analytics in evolutionary trend projection, demonstrating significant potential in dynamic transportation optimization. Among various machine learning paradigms, federated learning (FL) not only aligns well with IoV, but also provides it with privacy protection. Traditional FL faces single point of failure due to the existence of an aggregation center, so blockchain-based FL with multiple aggregators is utilized to mitigate this issue. Nevertheless, in such distributed environments, both aggregators and model parameters exposed to network are vulnerable to attacks, impeding the normal operation of FL. In this paper, for blockchain-enabled FL with multiple aggregators in IoV, we propose CLTSwNI&IA, the first non-interactive certificateless threshold signature with identifiable abort. This scheme eliminates certificate management and key escrow, adopts a blockchain-oriented approach by utilizing a fully distributed signing paradigm. Additionally, the proposed signing scheme is capable of identifying malicious FL aggregators during the entire process through distributed fine-grained verification and ensuring the integrity of aggregation results. Finally, theoretical and experimental comparisons with related literature demonstrate the advanced functionality and the acceptable efficiency of our approach.

The Internet of Vehicles (IoV) is an emerging technology that enhances traffic security and transportation efficiency by enabling smart, connected vehicles to communicate and exchange messages. IoV networks are a key component of intelligent transportation systems in smart cities. However, these networks are vulnerable to malicious vehicles that disseminate deceptive messages or impersonate legitimate entities, which compromises network security. These adversarial vehicles jeopardize the integrity and availability of the IoV network, exposing it to various security threats, including both insider and outsider attacks. Such attacks can severely undermine the trust and reliability of communication between legitimate vehicles. To address these challenges, we propose TMSU-IoV, a UAV-assisted trust management scheme that integrates identity authentication technique and trust evaluation mechanism to ensure secure communication of connected vehicles in IoV networks. To counteract outsider attacks, we introduce a certificateless signature-based authentication method that guarantees the authenticity of messages exchanged between vehicles and UAVs. To mitigate insider threats, we propose a quality of service (QoS)-based trust evaluation mechanism. This mechanism consists of a prior trust evaluation method and a posterior trust evaluation method, designed to enhance both the credibility and timeliness of trust evaluation for connected vehicles. Formal security analysis confirms that the TMSU-IoV effectively resists a variety of insider and outsider attacks. Performance evaluation experiments demonstrate that the TMSU-IoV can accurately assess the trust levels of connected vehicles and outperform traditional trust evaluation methods.

The existing approaches for skeleton-based action recognition based on graph convolutional networks (GCNs) primarily emphasize the construction of human skeletal structure by leveraging inherent connections. However, the static skeletal topology used across all action categories fails to capture discriminative relationships between joint pairs, while current graph structures struggle to model dynamic motion information, limiting their ability to represent both temporal and motion-specific dependencies. To address this limitation, we propose the decoupled static-dynamic co-occurrence graph convolution (DSDC-GConv), which specifically aims to learn and adapt the graph topology by refining the inter-frame and intra-frame joint dependencies through decomposed manner. Additionally, a multi-level context-aware module is proposed to comprehensively model the latent saliencies of multiple domains in skeletal sequences. This module refines the spatial nodes, temporal dynamics, channel-wise characteristics, and motional dependencies within the graph convolution block. Furthermore, a hierarchical densely connected temporal convolution is proposed to enhance the representation of local features through partial dense connections and enrich the temporal information during the convolution process. Findings from our evaluations on five large-scale benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, Kinetics Skeleton 400, Northwestern-UCLA, PKU-MMD) demonstrate the effectiveness and superiority of our proposed method over competing approaches, with an recognition accuracy of 93.0% and 97.1% on NTU RGB+D 60, 89.9% and 90.6% on NTU RGB+D 120, 38.6% and 63.4% on Kinetics Skeleton 400, 97.4% on Northwestern-UCLA, 97.6% and 63.6% on PKU-MMD.

Feature matching is widely applied in the image processing field. However, both traditional feature matching methods and previous deep learning-based methods struggle to accurately match the features with severe deformations and large displacements, particularly in gastroscopy scenario. To fill this gap, an effective feature matching framework named TransMatch is proposed, which addresses the largely displacements issue by matching features with global information leveraged via Transformer structure. To address the severe deformation of features, an effective bridging strategy with a novel bidirectional quadratic interpolation network is employed. This bridging strategy decomposes and simplifies the matching of features undergoing severe deformations. A deblurring module for gastroscopy scenario is specifically designed to address the potential blurriness. Experiments have illustrated that proposed method achieves state-of-the-art performance of feature matching and frame interpolation in gastroscopy scenario. Moreover, a large-scale gastroscopy dataset is also constructed for multiple tasks.

Extreme events (such as earthquakes, hurricanes, etc.) pose a dual challenge to the reliability and serviceability of Internet of Things (IoT) systems. With regard to this challenge, by publishing some tasks and then encouraging the public to assist in real-time data collection through their mobile terminals (namely, the mobile crowdsourcing-assisted IoT systems), is expected to play an important role in secondary disaster prevention and personnel rescue in extreme events. However, it has weaknesses in terms of security, flexibility, and efficiency. As an elegant solution, identity-based broadcast proxy re-encryption (PR-IBBE) enables flexible access authorization sharing and efficient broadcast distribution of encrypted tasks via the cloud. However, their security relies on fully trusted or semi-trusted cloud assumptions, which are hard to be implemented in real-world scenarios. And the cloud is more vulnerable in an emergency event since there is a lack of effective management. Motivated by that, we propose the verifiable PR-IBBE (VPR-IBBE) scheme, which realizes a cross-domain identity-based broadcast task file secure authorization access, and empowers the verifiability and reputability of re-encrypted ciphertext under the untrusted cloud setting. This mechanism ensures that the relevance between the re-encrypted ciphertext and the original ciphertext can be publically verified, so the cloud can defend itself if there is a malicious accusation of forging the re-encrypted ciphertext. Through rigorous formal security proofs, we demonstrate that VPR-IBBE attains the indistinguishability of ciphertext against selective identity chosen ciphertext attack (IND-sID-CPA), and is also resistant to the collusion attack between the untrusted cloud and the cooperative performer. Theoretical comparison and experimental results demonstrate the practicability of our VPR-IBBE scheme, as well as the superiority over representative related works.

Medical images often contain sensitive information, and one typical security measure is to encrypt medical images prior to storage and analysis. A number of solutions, such as those utilizing deep learning, have been proposed for medical image encryption and decryption. However, our research shows that deep learning-based encryption models can potentially be vulnerable to backdoor attacks. In this paper, a backdoor attack paradigm for encryption and decryption network is proposed and corresponding attacks are respectively designed for encryption and decryption scenarios. For attacking the encryption model, a backdoor discriminator is adopted, which is randomly trained with the normal discriminator to confuse the encryption process. In the decryption scenario, a number of subnetwork parameters are replaced and the subnetwork can be activated when detecting the trigger embedded into the input (encrypted image) to degrade the decryption performance. Considering the model performance degradation due to parameter replacement, the model pruning is also adopted to further strengthen the attacking performance. Furthermore, the image steganography is adopted to generate invisible triggers for each image; subsequently, improving the stealthiness of backdoor attacks. Our research on designing backdoor attacks for encryption and decryption network can serve as an attacking mode for such networks, and provides another research direction for improving the security of such models. This research is also one of the earliest works to realize the backdoor attack on the deep learning based medical encryption and decryption network to evaluate the security performance of these networks. Extensive experimental results show that the proposed method can effectively threaten the security performance both for the encryption and decryption network.

As a popular research topic in computer vision, video frame interpolation is widely used in video processing tasks. However, this task is often limited by slow processing speed or high memory consumption in practical applications. To address these drawbacks, a frame interpolation network focusing on motion regions named MFNet is proposed, which consists of a sampler for adaptive and efficient separation of motion regions from the background, a fine-grained module for direct approximation of intermediate streams, and a lightweight module for bi-directional optical stream fusion. Extensive experiments show that our MFNet achieves optimal accuracy on some frame interpolation tasks and is much faster than other state-of-the-art methods. In addition, transplantation of the core components of MFNet to other frame interpolation networks can significantly improve the performance.

Histopathology diagnosis is an important standard for breast tumors identifying. However, histopathology image analysis is complex, tedious and error-prone, due to the super-resolution image. In recent years, deep learning technology has been successfully applied to histopathology image analysis and made great progress. The well-known deep neural networks usually have tens of million parameters, which consume much memory to deploy the state-of-the-art model. In addition, deep neural networks rely on high-performance hardware resources, which impede the deployment of state-of-the-art model on portable equipment. In this work, a novel framework which consists of a weight accumulation method and a lightweight fast neural network (FastNet) was proposed for tumor fast identification (TFI) in mobile computer-assisted devices. The weight accumulation method was designed to obtain the tissue mask regions of interest and remove the useless background area in histopathology images, which greatly reduces the redundant computation cost. Furthermore, we proposed the lightweight FastNet to improve the computational efficiency on mobile devices. A novel attention loss function was designed and applied in FastNet. The attention loss function pays more attention on the positive samples and the indistinguishable samples, which greatly improves performance. The proposed FastNet was compared with three state-of-the-art methods commonly used for image classification and object detection. Experimental results indicated that FastNet achieves highest recall of 96.94%, highest F1 score of 97.33% and highest accuracy of 97.34%, besides least trainable parameters of 0.22M and smallest floating point operations of 210M FLOPs.

Abstract Human activity recognition (HAR) generates a massive amount of the dataset from the Internet of Things (IoT) devices, to enable multiple data providers to jointly produce predictive models for medical diagnosis. That the accuracy of the models is greatly improved when trained on a large number of datasets from these data providers on the untrusted cloud server is very significant and raises privacy concerns. With the migration of a deep neural network (DNN) in the learning experience in HAR, we present a privacy-preserving DNN model known as Multi-Scheme Differential Privacy (MSDP) depending on the fusion of Secure Multi-party Computation (SMC) and 𝜖-differential privacy, making it very practical since existing proposals are unable to make all the fully homomorphic encryption multi-key which is very impracticable. MSDP inputs a secure multi-party alternative to the ReLU function to reduce the communication and computational cost at a minimal level. With the aid of experimental verification on the four of the most widely used human activity recognition datasets, MSDP demonstrates superior performance with very good generalization performance and is proven to be secure as compared with existing ultramodern models without breach of privacy.

Mitigating adversarial deep learning attacks remains challenging, partly because of the ease and low cost in carrying out such attacks. Therefore, in this paper, we focus on the understanding of universal adversarial example attack on image classification models. Specifically, we seek to understand the difference(s) between adversarial examples in two adversarial datasets (DAmageNet and PGD dataset) and clean examples in ImageNet learned by the classification model, and whether we can use such findings to resist adversarial example attacks. We also seek to determine if we can retrain a discriminator to discriminate whether the input image is an adversarial example, using adversarial training. We then design a number of experiments (e.g., class activation map (CAM) analysis, feature map analysis, feature maps/filters changing, adversarial training, and binary classification model) to help us determine whether the universal adversarial dataset can be successfully used to attack the classification model. This, in turn, contributes to a better understanding of adversarial defenses over pretrained classification model from an interpretation perspective. To the best of our knowledge, this work is one of the earliest works to systematically investigate the interpretation of universal adversarial example attack on image classification models, both visually and quantitatively.

In hyperspectral image (HSI) classification, Convolutional Neural Networks (CNNs) have exhibited exceptional performance, owing to their hierarchical nonlinear modeling. However, their fixed square receptive field constrains their ability to effectively handle irregular image regions. Graph Convolution Networks (GCNs) have been introduced to learn irregular regions through correlations between adjacent pixels modeled as superpixel-based nodes, yet they lack pixel-level information. We propose a novel approach "Pixel-level with Covariance Pooling feature and Superpixel-level feature Fusion for Hyperspectral Image Classification" (PiCovS). Our method harnesses complementary spectral-spatial features at both pixel and superpixel levels to capture characteristics of both small-scale regular and large-scale irregular regions. We introduce a hybrid network that integrates and propagates features between image-level pixels and graph-level nodes using a graph encoder-decoder, effectively reconciling the differences between regular CNN and irregular GCN data representations. To enhance superpixel boundary learning, we modify the Manifold Simple Linear Iterative Clustering (M-SLIC) algorithm by incorporating texture feature information, resulting in refined superpixel representations. Additionally, we propose a novel covariance pooling mechanism with an attention mechanism within the CNN branch, enabling the capturing and utilization of holistic HSI information along spectral and spatial dimensions by exploiting second-order statistics throughout the network. Our comprehensive experiments showcase the efficiency and robustness of the proposed framework, achieving an impressive overall accuracy of 99.84%, 99.97%, 99.98%, and 81.96% on the Indian Pines, University of Pavia, Salinas, and the Houston University datasets, respectively. Remarkably, PiCovS excels even with limited training samples, outperforming other state-of-the-art methods in accuracy.

Domain adaptation methods train a model to find similar feature representations between a source and target domain. Recent methods leverage self-supervised learning to discover the analogous representations of the two domains. However, prior self-supervised methods have three significant drawbacks: (1) leveraging pretext tasks that are susceptible to learning low-level representations, (2) aligning the two domains using adversarial loss without considering if the extracted features are low-level representations, (3) the models are not flexible to accommodate various proportions of target labels, i.e., they assume target labels are always available. This paper presents a Generic Domain Adaptation Network (GDAN) to address these issues. First, we introduce a criterion based on instance discrimination to select appropriate pretext tasks to learn high-level domain invariant representations. Then, we propose a semantic neighbor cluster to align the two domain features. The semantic neighbor cluster implements a clustering technique in a feature embedding space to form clusters according to high-level semantic similarities. Finally, we present a weighted target loss function to balance the model weights according to the target labels. This loss function makes GDAN flexible for semi-supervised scenarios, i.e., partly labeled target data. We evaluate the proposed methods on four domain adaptation benchmark datasets. The experiment findings show that the proposed methods align the two domains well and achieve competitive results.

While 5G can facilitate high-speed Internet access and make over-the-horizon control a reality for unmanned aerial vehicles (UAVs; also known as drones), there are also potential security and privacy considerations, for example, authentication among drones. Centralized authentication approaches not only suffer from a single point of failure but they are also incapable of cross-domain authentication. This complicates the cooperation of drones from different domains. To address these limitations, a blockchain-based cross-domain authentication scheme for intelligent 5G-enabled Internet of drones is proposed in this article. Our approach employs multiple signatures based on threshold sharing to build an identity federation for collaborative domains. This allows us to support domain joining and exiting. Reliable communication between cross-domain devices is achieved by utilizing smart contract for authentication. The session keys are negotiated to secure subsequent communication between two parties. Our security and performance evaluations show that the proposed scheme is resistant to common attacks targeting Internet of Things (IoT) devices (including drones), as well as demonstrating its effectiveness and efficiency.

Access control mechanisms define the level of access to the resources among specified users. It distinguishes the users as authorized or unauthorized based on appropriate policies. Several traditional and hybrid access control models have been proposed in previous researches over the last few decades. In this study, we provide a detailed survey of access control models and compare the traditional and hybrid access control models based on their access control criteria. This survey focuses on the growing literature of access control models and summarizes it through comparative analysis, identifying limitations and illustrating the advantages of both traditional and hybrid models. This study will help the researchers to get a deep understanding of the traditional and hybrid access control models.

Medical practitioners generally rely on multimodal brain images, for example based on the information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis. Hence, to further utilize the 3D information embedded in such datasets, this paper proposes a multi-view dynamic fusion framework (hereafter, referred to as MVFusFra) to improve the performance of brain tumor segmentation. The proposed framework consists of three key building blocks. First, a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view. Second, the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views into an integrated method. Then, two different fusion methods (i.e., voting and weighted averaging) are used to evaluate the fusing process. Third, the multi-view fusion loss (comprising segmentation loss, transition loss, and decision loss) is proposed to facilitate the training process of multi-view learning networks, so as to ensure consistency in appearance and space, for both fusing segmentation results and the training of the learning network. We evaluate the performance of MVFusFra on the BRATS 2015 and BRATS 2018 datasets. Findings from the evaluations suggest that fusion results from multi-views achieve better performance than segmentation results from the single view, and also implying effectiveness of the proposed multi-view fusion loss. A comparative summary also shows that MVFusFra achieves better segmentation performance, in terms of efficiency, in comparison to other competing approaches.

The Internet of Health Things (IoHT) is a medical concept that describes uniquely identifiable devices connected to the Internet that can communicate with each other. As one of the most important components of smart health monitoring and improvement systems, the IoHT presents numerous challenges, among which cybersecurity is a priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues remain, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against a chosen-plaintext attack.

Resisting the adversarial sample attack on encrypted traffic is a challenging task in the Intelligent Transportation System. This paper focuses on the classification, adversarial samples attack and defense method for the encrypted traffic. To be more specific, the one-dimensional encrypted traffic data is firstly translated into the two-dimensional images for further utilization. Then different classification networks based on the deep learning algorithm are adopted to classify the encrypted traffic data. Moreover, various adversarial sample generation methods are employed to generate the adversarial sample to implement the attacking process on the classification network. Furthermore, the passive and active defense method are proposed to resist the adversarial sample attack: 1) the passive defense is used to denoise the perturbation in the adversarial sample and to restore to the original image; and 2) the active defense is used to resist the adversarial sample attack by leveraging the adversarial training method, which can improve the robustness of the classification network. We conduct the extensive experiments on the ISCXVPN2016 dataset to evaluate the effectiveness of classification, adversarial sample attacking and defending.

The need for medical image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients' medical imaging data. In this article, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three data sets, namely, the Montgomery County chest X-ray data set, the Ultrasonic Brachial Plexus data set, and the BraTS18 data set. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.

Internet of Medical Things (IoMT) can connect many medical imaging equipment to the medical information network to facilitate the process of diagnosing and treating doctors. As medical image contains sensitive information, it is of importance yet very challenging to safeguard the privacy or security of the patient. In this work, a deep-learning-based image encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DeepEDN, the cycle-generative adversarial network (Cycle-GAN) is employed as the main learning network to transfer the medical image from its original domain into the target domain. The target domain is regarded as “hidden factors” to guide the learning model for realizing the encryption. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve image decryption. In order to facilitate the data mining directly from the privacy-protected environment, a region of interest (ROI)-mining network is proposed to extract the interesting object from the encrypted image. The proposed DeepEDN is evaluated on the chest X-ray data set. Extensive experimental results and security analysis show that the proposed method can achieve a high level of security with a good performance in efficiency.

Due to its nonintrusive character, WiFi channel state information (CSI)-based activity recognition has attracted tremendous attention in recent years. Since activity recognition performance heavily relies on activity segmentation results, a number of activity segmentation methods have been designed, and most of them focus on seeking optimal thresholds to segment activities. However, these threshold-based methods are strongly dependent on designers' experience and might suffer from performance decline when applying to the scenario, including both fine-grained and coarse-grained activities. To address these challenges, we present DeepSeg, a deep learning-based activity segmentation framework for activity recognition using WiFi signals. In this framework, we transform segmentation tasks into classification problems and propose a CNN-based activity segmentation algorithm, which can reduce the dependence on experience and address the performance degradation problem. To further enhance the overall performance, we design a feedback mechanism, where the segmentation algorithm is refined based on the feedback computed using activity recognition results. The experiments demonstrate that DeepSeg acquires remarkable gains compared with state-of-the-art approaches.

The underlining task for fine-grained image recognition captures both the inter-class and intra-class discriminate features. Existing methods generally use auxiliary data to guide the network or a complex network comprising multiple sub-networks. They have two significant drawbacks: (1) Using auxiliary data like bounding boxes requires expert knowledge and expensive data annotation. (2) Using multiple sub-networks make network architecture complex and requires complicated training or multiple training steps. We propose an end-to-end Spatial Self-Attention Network (SSANet) comprising a spatial self-attention module (SSA) and a self-attention distillation (Self-AD) technique. The SSA encodes contextual information into local features, improving intra-class representation. Then, the Self-AD distills knowledge from the SSA to a primary feature map, obtaining inter-class representation. By accumulating classification losses from these two modules enables the network to learn both inter-class and intra-class features in one training step. The experiment findings demonstrate that SSANet is effective and achieves competitive performance.

Abstract With the universality and availability of Internet of Things (IoT), data privacy protection in IoT has become a hot issue. As a branch of attribute-based encryption (ABE), ciphertext policy attribute-based encryption (CP-ABE) is widely used in IoT to offer flexible one-to-many encryption. However, in IoT, different mobile devices share messages collected, transmission of large amounts of data brings huge burdens to mobile devices. Efficiency is a bottleneck which restricts the wide application and adoption of CP-ABE in Internet of things. Besides, the decryption key in CP-ABE is shared by multiple users with the same attribute, once the key disclosure occurs, it is non-trivial for the system to tell who maliciously leaked the key. Moreover, if the malicious mobile device is not revoked in time, more security threats will be brought to the system. These problems hinder the application of CP-ABE in IoT. Motivated by the actual need, a scheme called traceable and revocable ciphertext policy attribute-based encryption scheme with constant-size ciphertext and key is proposed in this paper. Compared with the existing schemes, our proposed scheme has the following advantages: (1) Malicious users can be traced; (2) Users exiting the system and misbehaving users are revoked in time, so that they no longer have access to the encrypted data stored in the cloud server; (3) Constant-size ciphertext and key not only improve the efficiency of transmission, but also greatly reduce the time spent on decryption operation; (4) The storage overhead for traceability is constant. Finally, the formal security proof and experiment has been conducted to demonstrate the feasibility of our scheme.

In recent years, ciphertext-policy attribute-based encryption (CP-ABE) has been recognized as a solution to the challenge of the information privacy and data confidentiality in cloud-assisted Internet-of-Things (IoT). Since the devices in cloud-assisted IoT are generally resource-constrained, the lightweight CP-ABE is more suitable for the cloud-assisted IoT. So how to construct the lightweight CP-ABE for the cloud-assisted IoT to achieve the fine-grained access control and ensure the privacy and confidentiality simultaneously is a prominent challenge. Thus, in this paper, we propose a constant-size CP-ABE scheme with outsourced decryption for the cloud-assisted IoT. In our scheme, the ciphertexts and the attribute-based private keys for users are both of constant size, which can alleviate the transmission overhead and reduce the occupied storage space. Our outsourced decryption algorithm is privacy-protective, which means the proxy server cannot know anything about the access policy of the ciphertext and the attributes set of the user during performing the online partial decryption algorithm. This will prevent the privacy from leaking out to the proxy server. And we rigorously prove that our scheme is selectively indistinguishably secure under the chosen ciphertext attacks (IND-CCA) in the random oracle model (ROM). Finally, by evaluating and implementing our scheme as well as other CP-ABE schemes, we can observe that our scheme is more suitable and applicable for cloud-assisted IoT.

Recently in IEEE Transactions on Cloud Computing (TCC), Yinet al.[5]designed a fine-grained query verification mechanism where a novel certificateless short signature scheme is proposed for validating the data of encrypted query results. Despite the authors alleged that their scheme achieves the existential unforgeability to ensure the authenticity of verification objects, we found that this scheme fails to resist the forgery attack. Specifically, through launching the concrete attacks, a malicious adversary can forge a signature on any verification object without being detected.

Internet-connected smart devices in, on, and around us, (e.g., embedded devices, wearable devices, and smart sensors) can collect human biometric features and facilitate identity authentication. Existing approaches are mainly based on pattern recognition and machine learning algorithms, which may not be capable of processing uncertain user information. Thus, focusing on the uncertainty of users' identity, this article proposes a fuzzy authentication system based on neural network and extreme value analysis. Specifically, we utilize biometric gait information of human body recognition. Our proposed authentication system is designed to implicitly authenticate users based on their gait, and can detect uncertain users and reject the authentication of unknown users. The performance is evaluated using an open dataset of 153 volunteers, where we manage to achieve a recognition accuracy rate of 98.4% and an error rate of unauthorized users at 6%.

Development of Internet of Vehicles (IoV) has aroused extensive attention in recent years. The IoV requires an efficient communication mode when the application scenarios are complicated. To reduce the verifying time and cut the length of signature, certificateless aggregate signature (CL‐AS) is used to achieve improved performance in resource‐constrained environments like vehicular ad hoc networks (VANETs), which is able to make it effective in environments constrained by bandwidth and storage. However, in the real application scenarios, messages should be kept untamed, unleashed, and authentic. In addition, most of the proposed schemes tend to be easy to attack by signers or malicious entities which can be called coalition attack. In this paper, we present an improved certificateless‐based authentication and aggregate signature scheme, which can properly solve the coalition attack. Moreover, the proposed scheme not only uses pseudonyms in communications to prevent vehicles from revealing their identity but also achieves considerable efficiency compared with state‐of‐the‐art work, certificateless signature (CLS), and CL‐AS schemes. Furthermore, it demonstrates that when focused on the existential forgery on adaptive chosen message attack and coalition attack, the proposed schemes can be proved secure. Also, we show that our scheme exceeds existing certification schemes in both computing and communication costs.

In this paper, we investigate physical (PHY) layer message authentication to combat adversaries with infinite computational capacity. Specifically, a PHY-layer authentication framework over a wiretap channel (W1; W2) is proposed to achieve information theoretic security with the same key. We develop a theorem to reveal the requirements/conditions for the authentication framework to be information-theoretic secure for authenticating a polynomial number of messages in terms of n. Based on this theorem, we design an authentication protocol that can guarantee the security requirements, and prove its authentication rate can approach infinity when n goes to infinity. Furthermore, we design and implement a feasible and efficient message authentication protocol over binary symmetric wiretap channel (BSWC) by using Linear Feedback Shifting Register based (LFSR-based) hash functions and strong secure polar code. Through extensive simulations, it is demonstrated that the proposed protocol can achieve high authentication rate, with low time cost and authentication error rate.

Most existing image enhancement methods function like a black box, which cannot clearly reveal the procedure behind each image enhancement operation. To overcome this limitation, in this paper, we design a progressive image enhancement framework, which generates an expected "good" retouched image with a group of self-interpretable image filters under the guidance of an aesthetic assessment model. The introduced aesthetic network effectively alleviates the shortage of paired training samples by providing extra supervision, and eliminate the bias caused by human subjective preferences. The self-interpretable image filters designed in our image enhancement framework, make the overall image enhancing procedure easy-to-understand. Extensive experiments demonstrate the effectiveness of our proposed framework.

Smart homes expects to improve the convenience, comfort, and energy efficiency of the residents by connecting and controlling various appliances. As the personal information and computing hub for smart homes, smartphones allow people to monitor and control their homes anytime and anywhere. Therefore, the security and privacy of smartphones and the stored data are crucial in smart homes. To protect smartphones from potential attacks, various built-in sensors can be utilized for user authentication/identification and access control to achieve enhanced security. In this paper, we propose a framework, smartphone sensor user identification (SSUI), in order to facilitate user identification based on the relationships between different types of sensor data and smartphone users. Specifically in SSUI, the time and frequency features are extracted and learned separately using convolution neural network (CNN). The CNN outputs are then processed using recurrent neural network, according to several time bins. Using both of our own dataset (collected from 17 participants) and a publicly available dataset (i.e., Heterogeneity Dataset for Human Activity Recognition), we demonstrate the effectiveness of the proposed SSUI framework, where we achieve an accuracy rate of over 91.45% in various scenarios.

As a cornerstone service for many Internet of Things applications, channel state information (CSI)-based activity recognition has received immense attention over recent years. However, recognition performance of general approaches might significantly decrease when applying the trained model to the left-out user whose CSI data are not used for model training. To overcome this challenge, we propose a semi-supervised generative adversarial network (GAN) for CSI-based activity recognition (CsiGAN). Based on the general semi-supervised GANs, we mainly design three components for CsiGAN to meet the scenarios that unlabeled data from left-out users are very limited and enhance recognition performance: 1) we introduce a new complement generator, which can use limited unlabeled data to produce diverse fake samples for training a robust discriminator; 2) for the discriminator, we change the number of probability outputs from k + 1 into 2k + 1 (here, k is the number of categories), which can help to obtain the correct decision boundary for each category; and 3) based on the introduced generator, we propose a manifold regularization, which can stabilize the learning process. The experiments suggest that CsiGAN attains significant gains compared to the state-of-the-art methods.

As the core of recommender systems, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, and so on. Benefiting from the strong representation power, neural networks have recently revolutionized the recommendation research, setting up a new standard for CF. However, existing neural recommender models do not explicitly consider the correlations among embedding dimensions, making them less effective in modeling the interaction function between users and items. In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF. We propose a novel and general neural collaborative filtering framework—namely, ConvNCF, which is featured with two designs: (1) applying outer product on user embedding and item embedding to explicitly model the pairwise correlations between embedding dimensions, and (2) employing convolutional neural network above the outer product to learn the high-order correlations among embedding dimensions. To justify our proposal, we present three instantiations of ConvNCF by using different inputs to represent a user and conduct experiments on two real-world datasets. Extensive results verify the utility of modeling embedding dimension correlations with ConvNCF, which outperforms several competitive CF methods.

The wireless body area networks (WBANs) have emerged as a highly promising technology that allows patients’ demographics to be collected by tiny wearable and implantable sensors. These data can be used to analyze and diagnose to improve the healthcare quality of patients. However, security and privacy preserving of the collected data is a major challenge on resource-limited WBANs devices and the urgent need for fine-grained search and lightweight access. To resolve these issues, in this paper, we propose a lightweight fine-grained search over encrypted data in WBANs by employing ciphertext policy attribute based encryption and searchable encryption technologies, of which the proposed scheme can provide resource-constraint end users with fine-grained keyword search and lightweight access simultaneously. We also formally define its security and prove that it is secure against both chosen plaintext attack and chosen keyword attack. Finally, we make a performance evaluation to demonstrate that our scheme is much more efficient and practical than the other related schemes, which makes the scheme more suitable for the real-world applications.

In this paper, we study physical layer message authentication with perfect security for wireless networks, regardless of the computational power of adversaries. Specifically, we propose an efficient and feasible authentication scheme based on low-density parity-check (LDPC) codes and ϵ-AU2hash functions over binary-input wiretap channel. First, a multimessage authentication scheme for noiseless main channel case is presented by leveraging a novel ϵ-AU2hash function family and the dual of large-girth LDPC codes. Concretely, the sender Alice first generates a message tag T with message M and key K by using a lightweight ϵ-AU2hash functions; then Alice encodes T to a codeword Xnwith the dual of large-girth LDPC codes; finally, Alice sends (M, Xn) to the receiver Bob noiselessly. An adversary Eve has infinite computational capacity, and he can obtain M and the output Znof the BEC with input Xn. Then, an authentication scheme over binary erasure channel and binary-input wiretapper's channel is further developed, which can reduce the noisy main channel case to noiseless main channel case by leveraging public discussion. We theoretically prove that, the proposed schemes are perfect secure if the number of attacks from Eve is upper bounded by a polynomial times in terms of n. Furthermore, the simulation results are provided to demonstrate that the proposed schemes can achieve high authentication rate with low time latency.

In this paper, we investigate multi-message authentication to combat adversaries with infinite computational capacity. An authentication framework over a wiretap channel (W_1, W_2) is proposed to achieve information-theoretic security with the same key. The proposed framework bridges the two research areas in physical (PHY) layer security: secure transmission and message authentication. Specifically, the sender Alice first transmits message M to the receiver Bob over (W_1, W_2) with an error correction code; then Alice employs a hash function (i.e., ε-AWU_2 hash functions) to generate a message tag S of message M using key K, and encodes S to a codeword X^n by leveraging an existing strongly secure channel coding with exponentially small (in code length n) average probability of error; finally, Alice sends X^n over (W_1, W_2) to Bob who authenticates the received messages. We develop a theorem regarding the requirements/conditions for the authentication framework to be information-theoretic secure for authenticating a polynomial number of messages. Based on this theorem, we propose and implement an efficient and feasible authentication protocol over binary symmetric wiretap channel (BSWC) by using Linear Feedback Shifting Register based (LFSR-based) hash functions and strong secure polar code. Through extensive experiments, it is demonstrated that the proposed protocol can achieve low time cost, high authentication rate, and low authentication error rate.

Convolutional Neural Networks (CNNs) have been widely used and achieve amazing performance, typically at the cost of very expensive computation. Some methods accelerate the CNN training by distributed GPUs those deploying GPUs on multiple servers. Unfortunately, they need to transmit a large amount of data among servers, which leads to long data transmitting time and long GPU idle time. Towards this end, we propose a novel hybrid parallelism architecture named "Wheel" to accelerate the CNN training by reducing the transmitted data and fully using GPUs simultaneously. Specifically, Wheel first partitions the layers of a CNN into two kinds of modules: convolutional module and fully-connected module, and deploys them following the proposed hybrid parallelism. In this way, Wheel transmits only a few parameters of CNNs among different servers, and transmits most of the parameters within the same server. The time to transmit data is significantly reduced. Second, to fully run each GPU and reduce the idle time, Wheel devises an alternate strategy deploying multiple workers on each GPU. Once one worker is suspended for receiving data, another one in the same GPU starts to execute the computing task. The workers in each GPU run concurrently and repeatedly like Wheels. Experiments are conducted to show the outperformance of the proposed scheme over the state-of-the-art parallel approaches.

Device authentication is a critical and challenging issue for the emerging Internet of Things (IoT). One promising solution to authenticate IoT devices is to extract a fingerprint to perform device authentication by exploiting variations in the transmitted signal caused by hardware and manufacturing inconsistencies. In this paper, we propose a lightweight device authentication protocol [named speaker-to-microphone (S2M)] by leveraging the frequency response of a speaker and a microphone from two wireless IoT devices as the acoustic hardware fingerprint. S2M authenticates the legitimate user by matching the fingerprint extracted in the learning process and the verification process, respectively. To validate and evaluate the performance of S2M, we design and implement it in both mobile phones and PCs and the extensive experimental results show that S2M achieves both low false negative rate and low false positive rate in various scenarios under different attacks.

Relational inference is a crucial technique for knowledge base population. The central problem in the study of relational inference is to infer unknown relations between entities from the facts given in the knowledge bases. Two popular models have been put forth recently to solve this problem, which are the latent factor models and the random-walk models, respectively. However, each of them has their pros and cons, depending on their computational efficiency and inference accuracy. In this paper, we propose a hierarchical random-walk inference algorithm for relational learning in large scale graph-structured knowledge bases, which not only maintains the computational simplicity of the random-walk models, but also provides better inference accuracy than related works. The improvements come from two basic assumptions we proposed in this paper. Firstly, we assume that although a relation between two entities is syntactically directional, the information conveyed by this relation is equally shared between the connected entities, thus all of the relations are semantically bidirectional. Secondly, we assume that the topology structures of the relation-specific subgraphs in knowledge bases can be exploited to improve the performance of the random-walk based relational inference algorithms. The proposed algorithm and ideas are validated with numerical results on experimental data sampled from practical knowledge bases, and the results are compared to state-of-the-art approaches.

Summary We consider a large‐scale online service system of placing resources geographically distributed over multiple regional cloud data centers. Service providers need to place the resources in these regions so as to maximize profit, accounting for demand granting revenues minus resource placement costs. The challenge is how to optimally place these resources to fulfill varying demands (e.g., multidimensional and stochastic demands) among these cloud data centers. Considering demand stochasticity will significantly increase time complexity of resource placement algorithm, resulting in inefficiency when handling a large number of resources. We propose a fast resource placement algorithm (FRP) to obtain the maximum resource revenue from distributed cloud systems. Experiments show that in scenarios with general settings, FRP can achieve up to 99.2% revenue of existed best solution while reducing execution time by two orders of magnitude. Therefore, FRP is an effective supplement to existing algorithms under time‐tense scheduling scenarios with a large number of resources. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Ring signature scheme protects the privacy while signer is signing. In the ring signature scheme, the signer can randomly choose verification keys of entities and generate a signature on behalf of these entities. The generated signature can be verified by anyone by inputting all these verification keys. Consequently, a ring signature convinces a verifier that one member from these entities produces this signature without revealing which one. This property is good for the signer as his identity is not leaked. However, the signer also can make use of this capacity to generate a malicious signature on behalf of a ring. Because of the unconditional anonymity of ring signature, this signer cannot be traced to be responsible for his malicious signing. Group signature can avoid this problem because the group manager in the group signature can trace the actual signer by using the trapdoor. However, the group is fixed from the beginning and it needs a complicated setup algorithm. Deniable ring signature was introduced by Komano et al., which allows to revoke the anonymity of actual signer without the manager's help if necessary. The actual signer can confirm his signing for anyone through the confirmation protocol. On the other hand, non‐signers in the ring can disavow this signing by the disavowal protocol. Therefore, the actual signer can be traced. However, Komano's scheme was proven in random oracles, and the traceability protocols (confirmation and disavowal protocols) are interactive. To improve Komano's construction, this work proposes a new efficient non‐interactive deniable ring signature scheme in the standard model. It is a kind of ring signature and therefore, it does not require a setup algorithm, and the ring in the scheme is flexible. Copyright © 2013 John Wiley & Sons, Ltd.

The identification of discriminative features of the Alzheimer's disease contributes to the diagnostic accuracy. Recently, the combination of different types of features has been actively used in the area of the AD classification. In this paper, we proposed a novel classification framework to jointly select features, which are extracted from the VBM analysis and texture analysis to distinguish between the AD and the NC. Furthermore, in order to capture robust discriminative features, we improve the feature subset selection by combining the SVM-RFE and covariance to take into account the relationship among features. In order to evaluate the proposed method, we have performed evaluations on the MRI acquiring from the ADNI database. Our experimental results showed the feature combination has better performance than the either morphometric features and texture features. Also, we demonstrated our method is better than the one without feature selection, PCA or others.

Recent studies have demonstrated that overlay localization can reduce the inter-ISP traffic efficiently, however, fully localized overlays generally impair the streaming quality. In this paper, we first investigate the effects of overlay localization and then present a novel ISP-friendly hierarchical overlay, termed HOPES, to achieve a favorable tradeoff between the inter-ISP traffic and the streaming quality. In HOPES, there are four components: (1) an algorithm for determining the abstract interconnections of all ISP domains; (2) a super peer selection scheme; (3) an algorithm for constructing the inter-ISP connections between super peers; (4) an algorithm for constructing the intra- ISP connections within each same ISP domain. Simulation results indicate that under various scenarios a significant reduction in the inter-ISP traffic is achievable while the streaming quality is enhanced by shrinking the inter-ISP depth and latency of the delivery path of each chunk.

Message Authentication Code (MAC) is a keyed function fKsuch that when Alice, who shares the secret K with Bob, sends fK(M) to the latter, Bob will be assured of the integrity and authenticity of M. Traditionally, it is assumed that the channel is noiseless. Unfortunately, Maurer showed that in this case an attacker can succeed with probability equation after authenticating ∓ messages, where H(K) is the entropy of K. In this paper, we consider the setting where the channel is noisy. Specifically, Alice and Bob are connected by a discrete memoryless channel (DMC) W1and a noiseless but insecure channel. In addition, there is a DMC W2between Alice and attacker Oscar. We regard the noisy channel as an expensive resource and define the authentication rate ρauthas the ratio of message length to the number n of channel W1uses. The security of this model depends on the channel coding for fK(M). A natural coding scheme is to use the secrecy capacity achieving code of Csiszár and Körner. Intuitively, this is also the optimal strategy. However, we propose a coding scheme that achieves a higher ρauth. Our crucial point is that under a secrecy capacity code, Bob can fully recover fK(M) while in our model this is not necessary as we only need to detect the existence of the modification. How to detect the malicious modification without recovering fK(M) is the main contribution of this work. We achieve this through random coding techniques.

Summary Both distance fraud attacks and relay attacks threaten radio‐frequency identification (RFID) applications but are hard to prevent. Existing approaches can neither avoid tags to response the rouge reader's challenges nor have the simultaneous feature to defend the two kinds of attacks. In a first step, this paper presents an improved distance‐bounding protocol that a tag deduces the distance to a reader and reports if the reader is honest or malicious through the output of trust values that can defend distance fraud. When multiple readers are synchronized and scheduled, we just logically threat them as one. So our solutions fix a flaw in prior work that may be leveraged by attackers to increase the successful rate of discovering relay attack. Secondly, we deploy trusted third party architecture to provide anonymity for tags in anonymous RFID systems without requiring tag identifiers. Existing distance‐based attack detection methods are not applicable in anonymous RFID systems because of the requirement of awareness of tag identifiers. This insight inspires Distance‐Bounding Trust Protocol (DBTP), which is for both distance fraud and relay attacks detection in anonymous RFID systems. DBTP can make correct decisions through trust values in accepting or rejecting a reader's challenge by establishing collaborations and trust relationship between one reader (verifier) and active tags (provers). We evaluate the performance of DBTP through theoretical analysis and extensive simulations. The results show that DBTP can detect both distance fraud and relay attacks, and it is effective to guarantee security for anonymous RFID systems. Copyright © 2015 John Wiley & Sons, Ltd.

To ensure the security and privacy of the patient's health status in the wireless body area networks (WBANs), it is critical to secure the extra-body communication between the smart portable device held by the WBAN client and the application providers, such as the hospital, physician or medical staff. Based on certificateless cryptography, this paper proposes a remote authentication protocol featured with nonrepudiation, client anonymity, key escrow resistance, and revocability for extra-body communication in the WBANs. First, we present a certificateless encryption scheme and a certificateless signature scheme with efficient revocation against short-term key exposure, which we believe are of independent interest. Then, a certificateless anonymous remote authentication with revocation is constructed by incorporating the proposed encryption scheme and signature scheme. Our revocation mechanism is highly scalable, which is especially suitable for the large-scale WBANs, in the sense that the key-update overhead on the side of trusted party increased logarithmically in the number of users. As far as we know, this is the first time considering the revocation functionality of anonymous remote authentication for the WBANs. Both theoretic analysis and experimental simulations show that the proposed authentication protocol is provably secure in the random oracle model and highly practical.

Email plays a very important role in our daily life. Much work have been put into practice on email network. Those studies mostly require real email network datasets and reliable models to analyze user information and understand the mechanisms of network evolution. However, much research work is constrained by the absence of real large-scale email datasets. Although email communication is ubiquitous, there are very few large-scale available email datasets satisfied different research purposes. Due to privacy policy and restricted permissions, it is arduous to collect a real large-scale email dataset in a short time. Various social network models are usually used to create synthetic email networks. However, these models focus on modeling several structural properties of network without considering user behaviour patterns. They are not appropriate to generate large-scale realistic synthetic email network datasets. Towards this end, we propose a probabilistic model by which we can construct large-scale synthetic email datasets with a small captured email log. What is more important is that the generated synthetic dataset matches real email network properties and individual communication patterns. Moreover, it has linear complexity, and can be paralleled easily. Experimental results on Enron dataset demonstrate the above benefits of our model.

Privacy protection attracts more and more attention from researchers to users. Due to the unreliability of mobile devices which often leads confidential information such as personal trajectory data divulged. We propose a new trajectory privacy protection method based on Semantic Anonymity Proxy proved to have some excellent characteristics. We alter the traditional ways which used to rely on the center anonymous server, adopting semantic anonymity and proxy request to construct the SAP tree on account of background map model to satisfy the location based services. The comparative results have shown that the new method achieves better performance than the classic ways on higher efficiency and quality.

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.

The simple, computationally efficient HB-like entity authentication protocols based on the learning parity with noise (LPN) problem have attracted a great deal of attention in the past few years due to the broad application prospect in low-cost RFID tags. However, all previous protocols are vulnerable to a man-in-the-middle attack discovered by Ouafi, Overbeck, and Vaudenay. In this paper, we propose a lightweight authentication protocol named LCMQ and prove it secure in a general man-in-the-middle model. The technical core in our proposal is a special type of circulant matrix, for which we prove the linear independence of matrix vectors, present efficient algorithms on matrix operations, and describe a secure encryption against ciphertext-only attack. By combining all of those with LPN and related to the multivariate quadratic problem, the LCMQ protocol not only is provably secure against all probabilistic polynomial-time adversaries, but also transcends HB-like protocols in terms of tag's computation overhead, storage expense, and communication cost.

BACKGROUND: Pairwise statistical significance has been recognized to be able to accurately identify related sequences, which is a very important cornerstone procedure in numerous bioinformatics applications. However, it is both computationally and data intensive, which poses a big challenge in terms of performance and scalability. RESULTS: We present a GPU implementation to accelerate pairwise statistical significance estimation of local sequence alignment using standard substitution matrices. By carefully studying the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous data access in the GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. We further extend the parallelization technique to estimate pairwise statistical significance using position-specific substitution matrices, which has earlier demonstrated significantly better sequence comparison accuracy than using standard substitution matrices. The implementation is also extended to take advantage of dual-GPUs. We observe end-to-end speedups of nearly 250 (370) × using single-GPU Tesla C2050 GPU (dual-Tesla C2050) over the CPU implementation using Intel Corei7 CPU 920 processor. CONCLUSIONS: Harvesting the high performance of modern GPUs is a promising approach to accelerate pairwise statistical significance estimation for local sequence alignment.

Signcryption is a cryptographic primitive that performs digital signature and public key encryption simultaneously, at lower computational costs and communication overhead than signing and encrypting separately. Recently, Yu et al. proposed an identity based signcryption scheme with a claimed proof of security. We show that their scheme is not secure even against a chosen-plaintext attack.

This paper introduces an efficient and spontaneous privacy-preserving protocol for vehicular ad-hoc networks based on revocable ring signature. The proposed protocol has three appealing characteristics: First, it offers conditional privacy-preservation: while a receiver can verify that a message issuer is an authorized participant in the system only a trusted authority can reveal the true identity of a message sender. Second, it is spontaneous: safety messages can be authenticated locally, without support from the roadside units or contacting other vehicles. Third, it is efficient: it offers fast message authentication and verification, cost-effective identity tracking in case of a dispute, and has low storage requirements. We use extensive analysis to demonstrate the merits of the proposed protocol and to compare it with previously proposed solutions.

We analyse different worm and patch propagation models along with the ones we have developed and evaluated as a part of our ongoing passive P2P worm & patch modelling project. This is followed by a brief discussion on worm detection mechanisms proposed by various authors. Towards the very end of this article, we propose a distributed framework for passive worm throttling in P2P networks and discuss its feasibility and efficiency keeping in view different design considerations.

Tiered wireless sensor networks (WSNs) have many advantages over traditional WSNs. However they are vulnerable to security attacks, especially those targeting at the storage nodes that bu er and process the data readings from sensors. In this paper, we propose a Secure Data Collection protocol - SDC to support time-based queries in tiered WSNs. With small overhead introduced to data communication, SDC protects both data confidentiality and data integrity. In particular it employs data co-commitment such that it can detect and evaluate the message dropping attacks in the network.

Existing replication strategies in unstructured P2P networks, such as square-root principle based replication, can effectively improve search efficiency. How to get optimal replication strategy, however, is not trivial. In this paper we show, through mathematical proof, that random replication strategy achieves the optimal results. By randomly distributing rather small numbers of item and query replicas in the unstructured P2P network, we can guarantee perfect search success rate comparable to exhaustive search with high probability. Our analysis also shows that the cost for such replication strategy is determined by the network size of a P2P system. We propose a hybrid P2P architecture which combines a lightweight DHT with an unstructured P2P overlay to address the problems of network size estimating and random peer sampling. We conduct comprehensive simulation to evaluate this design. Results show that our scheme achieves perfect search success rate with quite small overhead.