The Honeynet, as a typical active defense strategy, traps attackers' malicious behaviors, changing the asymmetric situation of network attack and defense. However, a static honeynet system would create a single deception environment, making it difficult to be effective against complex attacks. Additionally, the virtual honeypots are susceptible to exploitation by attackers, potentially leading to virtual escapes. To address the above issues, we propose a game theory-based method for secure deployment of mimic honeynet to enhance the deception capabilities of a honeynet and improve the security of honeypots. We construct multi-layer mimic honeypots and dynamically schedule the business executors within the mimic honeypot according to the virtualization layer's adjudication results, effectively preventing attacker escape attempts. We construct a Bayesian game model of attack and defense for the mimic honeynet, incorporating factors such as the attractiveness of the mimic honeypot and the similarity of the business layer. The aim of this game theory model is to determine the optimal mimic honeypot deployment strategy for a dynamic honeynet. Simulation results demonstrate that the mimic honeynet proposed in this paper effectively captures more malicious attack behaviors, reduces the probability of real hosts being attacked, and strengthens the security of the honeypot itself.

Honeypots based on deception technology offer a promising solution to address the asymmetry of attack and defense in the Internet of Things (IoTs). However, as the IoT security situation continues to evolve, attackers can identify honeypots by analyzing system characteristics and network behaviors, launching targeted virtual escape attacks that may exploit the honeypot as a stepping stone to compromise other systems. Once the IoT honeypot itself is successfully identified and attacked, the current defense measures typically rely on post-attack remediation. To address this challenge, we propose a mimic honeypot construction method based on incomplete information zero-sum stochastic game and Q-Learning. This method enhances the IoT honeypot’s deceptive capabilities while ensuring the security of the honeypot itself. Firstly, inspired by the concept of mimic defense, we design a dynamic heterogeneous redundancy honeypot (mimic honeypot), which contains multiple business executors composed of both business and virtualization layers. Secondly, we establish an incomplete information zero-sum stochastic game model to represent the honeypot attack-defense scenario. The Q-Learning algorithm is employed to solve for the Bayesian Nash equilibrium, enabling the mimic honeypot to adaptively adjust its deployment strategies based on the attacker’s observed actions. Finally, the experimental results demonstrate that the proposed mimic honeypot outperforms existing methods in terms of deceptive effectiveness and honeypot self-protection capabilities, significantly reducing the likelihood of honeypot compromise and ensuring robust network defense.

Security issues in the Internet of Things (IoT) are inevitable. Uncertain threats, such as known vulnerabilities and backdoors exist within IoT, and traditional passive network security technologies are ineffective against uncertain threats. To address the above issues, we propose an active defense adjudication method based on adaptive anomaly sensing for mimic IoT. The method constructs a mimic IoT active defense architecture, improving system security and reliability despite prevailing security threats. In addition, an intelligent anomaly sensing algorithm is integrated into the adjudication module of the mimic IoT active defense architecture to support arbitration. An adaptive anomaly sensing model based on multi-feature selection is used to determine the anomaly score of the IoT device outputs, and this model fully considers the reliability of the adjudication data and improves the accuracy of the adjudication. Finally, we conduct a comparative analysis of the proposed adjudication algorithm against three others via a mimic power communication IoT system as an application scenario. The experimental results show that our algorithm can improve security and reduce the failure rate of the mimic IoT system.

Abstract Turbo codes play a crucial role in wireless communication systems, and their compiled code structures are key factors affecting the performance of the entire communication system. As a result, the study of turbo compiled code structures has been a focal point for researchers. The iterative decoding of turbo code structures has multiple limitations and large storage resource consumption, leading to poor system anti‐interference ability and a rapid increase in BER. To address these issues, this paper proposes the mimic turbo compiled code structure (MTCCS) for wireless communication systems. MTCCS is based on the DHR idea, incorporating dynamic, heterogeneous, and redundancy characteristics. Dynamicity is achieved through a dynamic scheduling algorithm based on abnormal feedback information. Heterogeneity is achieved through a codec component collection design method based on intrinsic and extrinsic heterogeneity. Redundancy is achieved through a majority voting algorithm. At the beginning of information transmission, MTCCS randomly selects heterogeneous codecs from the heterogeneous codec collection to enter the runtime pool. After the information transmission is complete, the majority voting algorithm is used to adjudicate the multi‐mode output of the codecs, resulting in a relatively accurate decoding outcome. Meanwhile, the dynamic scheduling module calculates the abnormal feedback information of each codec and accordingly dynamically schedules the mimic turbo codecs to replace the abnormal ones. Through the above process, MTCCS realizes the adaptive compilation code and improves the anti‐interference ability of turbo code. Simulation experiments are conducted on MTCCS in both non‐interference and interference scenarios. Simulation experiments show that MTCCS introducing the DHR idea achieves a balance between anti‐interference and decoding performance. It effectively addresses the issue of poor anti‐interference ability in turbo codes, and the decoding performance of MTCCS is superior to that of the previous single conventional turbo codes.