Internet of Things realizes the leap from traditional industry to intelligent industry. However, it makes edge devices more vulnerable to attackers during processing perceptual data in real time. To solve the above problem, we use the zero‐sum game to build the interactions between attackers and edge devices and propose an antiattack scheme based on deep reinforcement learning. Firstly, we make the k NN‐DTW algorithm to find a sample that is similar to the current sample and use the weighted moving mean method to calculate the mean and the variance of the samples. Secondly, to solve the overestimation problem, we develop an optimal strategy algorithm to find the optimal strategy of the edge devices. Experimental results prove that the new scheme improves the payoff of attacked edge devices and decreases the payoff of attackers, thus forcing the attackers to give up the attack.

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device's spread capability and device's identification ability) on botnet formation. First, in terms of device's spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device's identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.

The construction of smart cities is the concentrated embodiment of the application of Internet of Things (IoT), which provides a variety of solutions to various problems faced by urban development and urban management. However, the openness, dynamics, and heterogeneity of city IoT increase the risk of attacks, especially the social attributes contained in such systems accelerate virus propagation. To reduce the risk and harm of virus propagation, it is of great significance to analyze the mode and the characteristics of virus propagation. This article proposes a dynamic virus propagation model [i.e., Ignorant-DKs- HN-Exposed-Pvirus-Spread-Recover (IDEPSR)], which focuses on two social attributes (i.e., intelligent device's propagation capability and identification ability). First, this article presents a new algorithm (i.e., DKs-HN) to measure the first attribute based on the evidence theory and an improved K-shell method. The DKs- HN merges the direct influence of a particular node and the indirect influence of 1-hop neighbors by utilizing the combination rules of the evidence theory. Second, this article proposes a Pvirus method to measure the second attribute based on the social hierarchy theory and the nonnegative matrix factor method. Every intelligent device can identify its trust relationship with other devices by using the Pvirus, and then the probability of virus activation lurking in devices can be predicted. Finally, a real data set is used to simulate a social-aware city IoT environment. Convincing experimental results show that the IDEPSR is more reasonable in design and good in performance. The IDEPSR performs better in controlling the virus propagation than the other five models.

Crowdsourcing uses collective intelligence to finish complicated tasks and is widely applied in many fields. However, the crowdsourcing dilemmas between the task requester and the task completer restrict the efficiency of system severely, e.g., the cooperation dilemma leads to the failure in the interactions and the quality of service dilemma results in the inability of task completer to provide high-quality service. Current research usually focuses on solving only one aforementioned dilemma and fails to integrate perfectly with the service architectural pattern of crowdsourcing systems. In this article, combined with the crowdsourcing interaction phase, we limit the objects that cause dilemma and propose a$\boldsymbol {t}$wo-stage$\boldsymbol {g}$ame$\boldsymbol {p}$ayoff$\boldsymbol {d}$ecision-making scheme (TGPD) to overcome these shortcomings. To solve the cooperation dilemma between the requester and the crowdsourcing platform, we first propose a dynamic payment method based on the reputation-quality rules for the task requester, and then develop a cos-evaluation algorithm to estimate platform’s cost, last design a co-determine algorithm to determine whether the platform adopts a cooperative strategy. To address the quality of service dilemma between the crowdsourcing platform and the workers, we first present an auction-screening method to estimate the reasonable recruitment range of workers which can be optimized by the result of cos-evaluation algorithm, and then use a reward distribution method to motivate workers to complete tasks with high quality and on time. The experimental results indicate that our new scheme successfully increases the worker’s and platforms’ payoffs at the same time, improves the accuracy of screening workers, enhances the worker’s quality of service, and decreases the platform’s cost.

The Intelligent Transportation System (ITS) is an important application area of the Cyber-Physical System (CPS). To further promote effective communication between vehicles, vehicular ad hoc networks (VANETs) have been widely used in the ITS. However, the communication efficiency in VANETs is not only affected by the external environment but also more vulnerable to malicious attacks. In order to address the above-mentioned issues, we propose a novel trust-based multicast routing protocol (TMR) to defend against multiple attacks and improve the routing efficiency. In the proposed trust model, direct trust is calculated based on Bayesian theory and indirect trust is computed according to evaluation credibility and activity. The fuzzy logic theory is used to fuzzify the direct and indirect trust values, and then the total trust value of the node is obtained by defuzzification. With the help of the obtained trust values, malicious vehicle nodes are eliminated in the processes of route establishment and route maintenance, and finally, the network establishes trusted and efficient routes for data delivery. Comprehensive simulation experiments show that our new protocol can effectively improve the transmission rate of data packets at the expense of a slight increase in end-to-end delay and control overhead.

A mobile ad hoc network (MANET) is a self-configuring network of mobile nodes connected by wireless links without fixed infrastructure, which is originally designed for a cooperative environment. However, MANETs are subjected to a variety of attacks by malicious nodes, in particular for attacks on the packet routing. Compared with traditional cryptosystem based security mechanisms, trust-enhanced routing protocol could provide a better quality of service. In this study, we abstract a basic decentralized effective trust inference model based on node's behavior assessment, where each peer assigns a trust value for a set of peers of interest. In this model, we introduce the 'voting' mechanism to access the recommending experience (or ratings), in order to reduce the cost of the algorithm design and the system overhead. Then combined with this trust model, a novel trust-enhanced multicast routing protocol (TeMR) is proposed. This new protocol introduces the group-shared tree strategy, which establishes more efficient multicast routes since it uses 'trust' factor to improve the efficiency and robustness of the forwarding tree. Moreover, it provides a flexible and feasible approach in routing decision making with trust constraint and malicious node detection. Experiments have been conducted to evaluate the effectiveness of the proposed protocol.

In traditional identity-based encryption schemes, security will be entirely lost once secret keys are exposed. However, with more and more use of mobile and unprotected devices, key exposure seems unavoidable. To deal with this problem, we newly propose a forward-secure identity-based public-key encryption scheme. In this primitive, the exposure of the secret key in one period doesn't affect the security of the ciphertext generated in previous periods. Any parameter in our scheme has at most log-squared complexity in terms of the total number of time periods. We also give the semantic security notions of forward-secure identity-based public-key encryption. The proposed scheme is proven semantically secure in the standard model. As far as we are concerned, it is the first forward-secure identity-based public-key encryption scheme without random oracles.

A group signature scheme allows a member of a given group to anonymously sign a message on behalf of the group. In the case of a later dispute, a designated group manager can open the signature, revoke the anonymity and identify the original signer. This paper presents a new and practical approach to group signatures. A trusted on-line third party is introduced in our scheme to help to realize (1) the "join" of group members, (2) the "opening" of group signatures, and (3) the "revocation" of group membership. Based on classical RSA, a practical group signature scheme is constructed. It is shown that our scheme satisfies all the security requirements of a secure group signature scheme, and the introduction of this third party makes our scheme much more simple and practical than the previous schemes of this kind.

We present a new method to forge ElGamal signatures with the cases that the secret key parameters of the system are not known under the chosen signature messages. The attacker can forge the signature substituting the right signature, and also attack the right secret key without depending on the computation of discrete logarithm. With the attacking probability cryptanalysis, it is found that the cryptosystem can be attacked successfully in some conditions.

We propose an identity (ID)-based signature from m-torsion groups of super-singular elliptic curves or hyper-elliptic curves. It is secure against existential forgery under adoptively chosen message and ID attack in the random oracle model. Due to the nice properties of m-torsion groups and Weil pairing, it turns out that the proposed scheme is simple, efficient and has the good property of addition. Based on this ID-based signature scheme, we propose an ID-based threshold signature scheme and show that it has the security properties of unforgeability and robustness.

It is well known that the algebraic expression of the advanced encryption standard (AES) Rijndael S-box is very simple and only 9 terms are involved. In this paper a new algebraic method is proposed to illustrate the reason why AES S-box algebraic expression is very simple, which corrects all the available inaccurate illustrations. An improved AES S-box is also presented to improve the complexity of AES S-box algebraic expression with terms increasing from 9 to 255 and algebraic degree invariable. The improved AES S-box also has good properties of Boolean functions in SAC and balance, and is capable of attacking against differential cryptanalysis with high reliable security.