In cyber-physical systems (CPS) and Internet of Things (IoT) environments, the security and efficiency of data aggregation are critical under resource constraints and partial trust conditions. This paper presents DA-RWFHE, a dual-layer framework that integrates Fully Homomorphic Encryption (FHE) with Reversible Watermarking (RW) to support aggregation in the encrypted domain and integrity verification during transit. The design emphasizes system robustness and adaptability, ensuring reliable performance in dynamic and failure-prone environments. Security analysis confirms that DA-RWFHE is resilient against manipulation, replay, collusion, and denial-of-service attacks. Extensive OMNeT++ simulation results show that DA-RWFHE outperforms existing schemes, such as APPA, MFEG, ECBDA, and BAMDD, improving bandwidth by 26.18 Mbps, reducing latency by 20.42 ms, and lowering energy consumption by 1.050 W. These results demonstrate that DA-RWFHE provides a lightweight, verifiable, and scalable solution for secure aggregation in distributed systems, particularly in resource-constrained environments. While DA-RWFHE performs well in simulations, it still faces challenges in real-world applications, particularly regarding encryption computational complexity on low-power devices and node topology changes. To address these challenges, the paper proposes optimization strategies, with future work planned for hardware validation to further enhance the feasibility of this approach.

With the development of Internet of Things (IoT), more and more devices are connected to the network. Moving target defense (MTD) provides a security solution for many IoT network devices. However, since MTD is deployed in a packet switching network, there are problems, such as packet delay and congestion. Traditional follow-up synchronization scheme is not competent for high-rate change of MTD. This poses a serious security threat to IoT devices because slow rate changes are powerless in defending against highly adversarial network attacks. In this article, we innovatively propose an MTD synchronization method, which uses synchronization certificates to complete synchronization. Furthermore, we establish a fast switching MTD (CMTD) model based on cryptography fundamental logics (CFLs). It improves MTD switching rate to milliseconds and solves the problem of synchronization in high-speed hopping with high covert requirements. Theoretical analysis and experimental verification show that our method effectively improves the availability and concealment of IoT devices under high-speed hopping. It is of great significance for IoT security protection under high-intensity cyber attacks.

Nowadays, the proliferation of open Internet of Things (IoT) devices has made IoT systems increasingly vulnerable to cyber attacks. It is of great practical significance to solve the security issues of IoT systems. Drawing inspiration from the concept of “Pawn Promotion” in chess, this article proposes a cyber deception defense system based on “Chess Puzzle” (CP-CDD), which enhances the security of IoT systems by continuously changing the real and fake attributes of services. First, we design a distributed election control device scheme to address the security concerns arising from centralized control in the IoT system. Second, we introduce an adaptive differential evolution cyber deception strategy (ADECDS) optimization method, aiming to derive effective cyber deception defense strategies for the CP-CDD system. Specifically, we design two key components of cyber deception strategy evolution: 1) service strategy coding and 2) fitness function, and describe the ADECDS algorithm. Finally, the security and effectiveness of the CP-CDD system is proven through attack and defense experiments conducted in a real-world environment.

Dynamic Heterogeneous Redundancy (DHR) has been considered as a proactive defense architecture against unknown vulnerabilities and backdoors. It is a heterogeneous, fault-tolerant, and redundant system, which ensures the security by constantly switching heterogeneous executors. However, a limited number of heterogeneous executors can be merely switched and combined statically. It is difficult to cope with persistent threats. In this paper, we propose an evolutionary DHR system, add evolutionary sub-strategies of executors to solve this problem, optimizing defense mechanisms of DHR. To demonstrate theoretical validity, a game-theoretic analysis of the DHR security mechanism is performed under dynamic incomplete information, and the dilemma of defense is discussed. We construct a DHR game model based on evolutionary, and the results are extended to the general case to analyze the Bayesian equilibrium when each service strategy has a different number of evolved sub-strategies. In addition, the correlation factor is added to the game to investigate the impact of the correlation between different heterogeneous executors on defense results. Finally, Gambit and NS2 simulation experiments for the proposed method are shown. To verify the applicability of the physical system, a prototype DHR system based on dynamic evolution was constructed. We collect key configuration information and modified them randomly. It ensures that the information scanned by the attacker is different each time. Finally, the effectiveness of the physical system is verified by nmap and metasploit tools.

Dynamic Heterogeneous Redundancy (DHR) is a proactive defense architecture. A limited number of heterogeneous executors can be switched and combined, ensuring the security of systems. In this paper, we demonstrate the DHR architecture defense mechanism by the game theory. Combining incomplete information dynamic game with DHR, the attacker and the legitimate user are treated as the visitors and the defender as the server. Then, a DHR game model under incomplete information is proposed, which formally describes strategies and payoffs of all players in the game. Finally, Gimbit and NS2 simulation experiments verify the effectiveness and correctness of the game reasoning.

In order to protect information privacy and ensure user information security, in view of the obvious centralization of the existing identity authentication technologies such as Public Key Infrastructure(PKI) and Identity-Based Encrypted(IBE), this paper proposes an efficient authentication strategy that applies Cryptography Fundamental Logics(CFL) identity authentication technology to Mobile Crowd Sensing(MCS) system, which can complete the authentication between Task Publisher, Cluster Head and Task Participant without the participation of a third-party center. Firstly, this paper introduces to use CFL technology to solve the problem of identity authentication relying on the central server; Secondly, an algorithm combined with MCS system is proposed to solve the decentralization of authentication process; Finally, the Average System Response Time and System Throughput of the three technologies are obtained through simulation experiments, analyzed and compared. The result shows that: this strategy has obvious advantages, it can faster and more secure the identity authentication.

Recently recommender systems become more and more significant in the daily life such as event recommendation, content recommendation and commodity recommendation, and so forth. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration to improve the recommendation accuracy. In this paper, we propose a Trust-Aware GAN with recurrent neural network (RNN) for RECommender systems named TagRec, which makes use of the user trust information for top-N recommendation. In the framework, the discriminative model is a multilayer perceptron to distinguish whether a sample is from the real data or fake data generated by the generative model. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a RNN with long short-term memory cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real-word data sets to validate the effectiveness of the TagRec by comparing it with the benchmarks.

Radio frequency identification (RFID) systems can be applied to efficiently identify the missing items by attaching them with tags. Prior missing tag identification protocols concentrated on identifying all of the tags. However, there may be some scenarios in which we just care about the key tags instead of all tags, making it inefficient to merely identify the missing key tags due to the interference of replies from the ordinary tags (i.e., nonkey tags). In this paper, we propose to investigate the problem of efficiently and completely identifying the missing key tags for anonymous RFID systems in which the tag privacy is required to be well protected. First, we propose a vector-based missing key tag identification protocol called VEKI. Then we propose an improved protocol called iVEKI, which consists of two phases: 1) ordinary tag deactivation and 2) missing key tag identification. The parameters of the proposed VEKI and iVEKI protocols are theoretically optimized to maximize the time efficiency. Finally, we conduct extensive simulations to evaluate the proposed VEKI and iVEKI protocols and the simulation results illustrate that they outperform other existing protocols in terms of execution time.

Spam is an imperative problem to the email communication today. Different users may have different views on judging spam which makes it difficult to filter spam from normal emails for email server. We found users with similar interest may have similar opinions. So in this paper, we proposed a spam filtering approach in which a collaborative and personalized spam filter based on social network is developed. The key idea is to enable users to push spam reports to their social network friends with similar interest, which reflects collaboration and personalization. Our proposal takes advantage of push technology to share user's individual spam knowledge with others via social network, which utilizes wisdom of crowds to resist spam. According to interest similarity among users, a user can determine whether to push spam reports to his friends with the purpose of taking user's individual interest into consideration. We integrate an interest-based spam filter with a basic Bayesian filter to discriminate spam from legitimate emails. The evaluation of our proposal shows that it significantly improves the performance compared with Bayesian filter according to the accuracy rate.