Residual Network (ResNet) is a distinguished network structure in deep learning, and its layers can be profound. We theoretically explore the mathematics characteristics of the ResNet, in particular, to pay attention to the gradient information, which is a powerful and straightforward mathematical tool for analysing the properties of ResNet, such as the gradients of the loss function with respect to the input and the weight parameters and the gradient of the entry of the logits with respect to the input. A theorem about the explicit matrix expression of gradients in Resnet is given in this work. A rigorous mathematical and logical derivation of the theorem is obtained in detail by the matrix derivative definition and matrix differentiation. We further provide explicit matrix expressions of some deep learning algorithms in ResNet, including backpropagation, gradient-based adversarial attacks, and gradient-based saliency maps. Furthermore, the reasons why the ResNet network works are analysed. Finally, experimental results are provided to verify the correctness and efficiency of the proposed theorem.

In this paper, we study the coordinated multicast and unicast transmission for cellular-based vehicle-to-vehicle (C-V2V) massive multiple-input multiple-output (MIMO) in the scenario where only imperfect channel state information is available at the base station side and the transmitter in each V2V communication pair. The precoding design problem is a weighted ergodic sum-rate maximization problem with the imperfect CSI including channel mean and variance information known, and the objective function is the weighted sum of the achievable ergodic unicast rate for all the V2V pairs and the achievable ergodic multicast rate for all the cellular users. The minimize-maximize (MM) algorithm and the deterministic equivalent method are utilized to solve the problem and reduce the computational complexity. The simulation results demonstrate the significant improvements of the proposed coordinated communication method in the system spectral efficiency.

Source location privacy (SLP) protection is an emerging research topic in wireless sensor networks. Because the source location represents the valuable information of the target being monitored and tracked, it is of great practical significance to achieve a high degree of privacy of the source location. Although many studies based on phantom nodes have alleviates the protection of SLP to some extent. It is urgent to solve the problems, such as complicate the ac path between nodes, improve the centralized distribution of phantom nodes near the source nodes and reduce the network communication overhead. In this paper, protection scheme based on sector phantom routing (PSSPR) routing is proposed as a visible approach to address SLP issues. We use the coordinates of the center node V to divide sector domain, which act an important role in generating a new phantom node. The phantom nodes perform specified routing policies to ensure that they can choose various locations. In addition, the directed random route can ensure that data packets avoid the visible range when they move to the sink node hop by hop. Thus, the source location is protected. Theoretical analysis and simulation experiments show that this protocol achieves higher security of source node location with less communication overhead.

Artificial intelligence is developing rapidly in the direction of intellectualization and humanization. Recent studies have shown the vulnerability of many deep learning models to adversarial examples, but there are fewer studies on adversarial examples attacking facial expression recognition systems. Human–computer interaction requires facial expression recognition, so the security demands of artificial intelligence humanization should be considered. Inspired by facial expression recognition, we want to explore the characteristics of facial expression recognition adversarial examples. In this paper, we are the first to study facial expression adversarial examples (FEAEs) and propose an adversarial attack method on facial expression recognition systems, a novel measurement method on the adversarial hardness of FEAEs, and two evaluation metrics on FEAE transferability. The experimental results illustrate that our approach is superior to other gradient-based attack methods. Finding FEAEs can attack not only facial expression recognition systems but also face recognition systems. The transferability and adversarial hardness of FEAEs can be measured effectively and accurately.

With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situations, some selected instances are redundant and some important instances are removed, increasing storage space and reducing efficiency. Therefore, a new representativeness of instance is proposed and considers not only the influence of all instances of the same class on the selected instance but also the influence of instances of different classes on the selected instance. Moreover, it considers the influence of instances of different classes as an advantageous factor. Based on this representativeness, two instance selection algorithms are proposed to handle balanced and imbalanced data problems for intrusion detection. One is a representative-based instance selection for balanced data, which is named RBIS and selects the same proportion of instances from each class. The other is a representative-based instance selection for imbalanced data, which is named RBIS-IM and selects important majority instances according to the number of instances of the minority class. Compared with other algorithms on the benchmark data sets of intrusion detection, experimental results verify the effectiveness of the proposed RBIS and RBIS-IM algorithms and demonstrate that the proposed algorithms can achieve a better balance between accuracy and reduction rate or between balanced accuracy and reduction rate.

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages.

Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.

Machine learning plays an important role in constructing intrusion detection models. However, the information era is an era of data. With the continuous increase in data size and the growth of data dimensions, the ability of a single classifier is becoming limited in predicting samples. In this paper, we present an ensemble method using random subspace in which an extreme learning machine (ELM) is chosen as the base classifier. To optimize the ensemble model, an ensemble pruning method based on the bat algorithm (BA) is proposed. Meanwhile, a fitness function based on the accuracy and diversity of an ensemble is defined in the BA to obtain an improved classifier subset. Three public datasets, the KDD99, NSL and Kyoto datasets, are adopted to assess the robustness of the method. The empirical results indicate that the ensemble method based on random subspace can improve the accuracy and robustness over the use of an individual ELM. The results also show that compared with when all the sub-classifiers are used in the ensemble, the pruning framework can not only achieve comparable or better performance but also save substantial computing resources in an intrusion detection system (IDS).

A major challenge in distributed mobile networks is network integrity, resulting from short link duration and severe transmission collisions. This paper analyzes the impact of link duration and transmission collisions on a range of on-the-fly authentication protocols, which operate based on predistributed keys and can instantly verify and forward messages. All unexpired messages within a link duration can be verified retrospectively, once the keys are matched on-the-air. We develop a new general 4D Markov model which, apart from the first three dimensions modeling a cycle of the protocols, is able to unprecedentedly capture unexpired messages between cycles in the fourth dimension. Validated by simulation, our analysis reveals that the on-the-fly authentication is efficient under short link duration, but is susceptible to transmission collisions. The authentication requires holistic cross-layer designs of retransmission and rekeying. The proposed model is able to facilitate the design of the protocol parameters, which allows the protocols to significantly outperform the state of the art.

Summary Cloud servers provide cloud users with storage service and allow cloud users to access their files anytime. To guarantee security of the stored files, auditors need to periodically verify data block correctness. In the existing integrity verification schemes, there are few protocols to support the users' identity anonymity and the data block dynamic operation simultaneously. In this paper, we present an efficient and anonymous identity‐based integrity auditing protocol, which supports data dynamic operation and can be extended to support batch auditing in the multifile or multiuser setting. Our scheme not only resists forgery, replace, and replay attacks but also maintains users' anonymity, which is not discussed in other related techniques. The computation efficiency of auditor is improved a lot. Comparing with Zhang's efficient identity‐based public auditing scheme, our scheme is more suitable for actual application scenario with large‐scale storage system.

Shilling attacks have been a significant vulnerability to collaborative filtering based recommender systems recently. There are various studies focusing on detecting shilling attack users and developing robust recommendation algorithms against shilling attacks. Although many studies have been devoted in this area, few of them use soft co‐clustering and consider both labelled and unlabelled user profiles. In this work, the authors explore the benefits of combining soft co‐clustering algorithm with user propensity similarity method and present a soft co‐clustering with propensity similarity model or CCPS for short, to detect shilling attacks. Then they perform experiments using MovieLens dataset and Jester dataset to analyse it with respect to shilling attack detection to demonstrate the effectiveness of CCPS model in detecting traditional and hybrid shilling attacks and enhance the robustness of recommender systems.

This paper studies the problems of “security meridian-collateral” and “security confrontation” systematically and refreshes the traditional concept of security. On the one hand, based on the model of “meridians” in Chinese traditional medicine for the first time, this paper proves the following results strictly by the probability method. There is a complete “meridian-collateral diagram” in Internet of things and any finite system, so that any “sickness” of the system can be cured effectively. On the other hand, this paper studies the network attack and defense from the perspective of the information theory. Through mathematical modeling, based on the famous Shannon's coding theorem, the research on the ability problem of attacker and defender is transformed into the research on the channels of attacker and defender. From the perspective of the channel capacity in information theory for the first time, this paper gives the reachable theoretical limitation of the attack ability of hacker and the defend ability of honker precisely.

Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights.

Challenges of authentication in decentralized mobile networks arise from frequently changing topologies and unreliable contention-based transmissions. We propose a new protocol to speed up authentications, reduce communication costs, and support opportunistic routing under fast-changing topologies. Key pairs are predistributed across the network. Nodes that predistributed the same pair can instantly verify and route messages for each other in an opportunistic and cooperative fashion, combating fast-changing topologies. We also enable a node to increasingly combine unauthenticated messages and a new message for signature or message authentication code generation, while trying different keys on-the-fly. The messages can be verified altogether, once a key is matched. The communication overhead, thus, becomes independent of the number of keys tried. Closed-form expressions for authentication rate, delay, and throughput are derived through a new three-dimensional Markov model. Validated by simulations, analytical results corroborate the robustness of the proposed protocol against changing topologies, as well as the substantially improved resistance to collusion attacks, as compared with the state of the art.

The Cramer-Shoup cryptosystem has attracted much attention from the research community, mainly due to its efficiency in encryption/decryption, as well as the provable reductions of security against adaptively chosen ciphertext attacks in the standard model. At TCC 2005, Vasco et al. proposed a method for building Cramer-Shoup like cryptosystem over non-abelian groups and raised an open problem for finding a secure instantiation. Based on this work, we present another general framework for constructing Cramer-Shoup like cryptosystems. We firstly propose the concept of index exchangeable family (IEF) and an abstract construction of Cramer-Shoup like encryption scheme over IEF. The concrete instantiations of IEF are then derived from some reasonable hardness assumptions over abelian groups as well as non-abelian groups, respectively. These instantiations ultimately lead to simple yet efficient constructions of Cramer-Shoup like cryptosystems, including new non-abelian analogies that can be potential solutions to Vasco et al.'s open problem. Moreover, we propose a secure outsourcing method for the encryption of the non-abelian analog based on the factorization problem over non-commutative groups. The experiments clearly indicate that the computational cost of our outsourcing scheme can be significantly reduced thanks to the load sharing with cloud datacenter servers.

Advanced Persistent Threat (APT) imposes increasing threats on cyber security with the developing network attack technologies. APT is a highly interactive, specifically targeted and extremely harmful network-centric attack, which employs various technologies to evade detection during attacks leading to the result that victims will not be aware of attacks until they suffer from tremendous losses. Since command and control (C&C) is an essential component during the lifetime of APT, the detection of it is a practical measure to defend against the APT. In this paper, we analyze the features of C&C in APT and find that the HTTP-based C&C is widely used. Based on the analysis results, we propose a new feature of C&C, i.e., independent access, to characterize the difference between C&C communications and normal HTTP requests. Applying the independent access feature into DNS records, we implement a novel C&C detection method and validate it on public dataset. As a new feature of C&C, its advantages and drawbacks are also analyzed.

Summary Verifying the integrity of outsourced data is an important issue in cloud storage. Recently, many integrity verify protocols have been proposed that can support the public verifiability, data dynamics, and privacy against verifiers but involve a third‐party auditor's participation. In this work, we propose a novel identity‐based storage management and integrity verify protocol for secure outsourcing in multi‐cloud. From theoretical analysis and a lot of simulation experiments, we show our proposed protocol is secure and efficient, without using any trusted organizer. Furthermore, the protocol enjoys many important security attributes including prevention of various attacks, user anonymity, local password verification, and so on. Copyright © 2015 John Wiley & Sons, Ltd.

Biological epidemic models, widely used to model computer virus propagations, suffer from either limited scalability to large networks, or accuracy loss resulting from simplifying approximations. In this paper, a discrete-time absorbing Markov process is constructed to precisely characterize virus propagations. Conducting eigenvalue analysis and Jordan decomposition to the process, we prove that the virus extinction rate, i.e., the rate at which the Markov process converges to a virus-free absorbing state, is bounded. The bounds, depending on the infection and curing probabilities, and the minimum degree of the network topology, have closed forms. We also reveal that the minimum curing probability for a given extinction rate requirement, specified through the upper bound, is independent of the explicit size of the network. As a result, we can interpret the extinction rate requirement of a large network with that of a much smaller one, evaluate its minimum curing requirement, and achieve simplifications with negligible loss of accuracy. Simulation results corroborate the effectiveness of the interpretation, as well as its analytical accuracy in large networks.

This paper presents a scheme of a multiple-image compressed encryption based on the compressive holography technique. Computer generate hologram (CGH) is implemented to record multiple images simultaneously into an encrypted hologram. Because its two-dimensional (2D) Fourier transform (FT) result is analogous a partial 3D Fourier transform sampling, the 2D FT result can be compressed by a nonuniform sampling accompanied with a quantization. The encryption and compression processes agrees with the requirement of the compressive sensing and composes the compressive holography. Therefore, the decryption is solved by a minimization. It remains the sparsity of the recovered natural images in the wavelet basis. Meanwhile, a total-variation regularization and a nonnegative constraint is employed to extract images with edge preserved and nonnegative gray scale, respectively. Experiments are conducted to demonstrate the feasibility of the multiple- image compressed encryption.

The following results are proven in this paper: 1) neither periodic autonor cross-correlation of the truncated Costas optical orthogonal code (TC OOC) is upper bounded by 1; 2) TC OOC is a class of (/spl omega/(2p-3), /spl omega/,2,2) optical orthogonal codes.