Honeypots, which serve as active defense mechanisms, have historically played pivotal roles in cyberspace offensive and defensive countermeasure scenarios. However, with the advancement of honeypot recognition technologies, their effectiveness in real-world network defense has gradually diminished. In response, moving target defense (MTD) has recently solidified its position as a proactive cybersecurity strategy and a critical research frontier. MTD leverages heterogeneous, redundant deployments of service resources and randomization techniques to disrupt attack methods. However, despite their advantages, MTD systems face challenges related to high resource consumption. To address these limitations, we propose a moving target defense based on stochastic games and honeypots (GH-MTD) framework. This framework consists of four key modules: traffic detection, gaming, MTD, and honeynet. Firstly, malicious traffic is identified through a deep learning-based detection method. Secondly, a zero-sum game model is constructed to capture the decision-making dynamics between defenders and attackers in the context of moving target defense. Subsequently, a cross-scenario adaptive MTD module is designed to route different types of traffic to corresponding virtual server groups. Finally, a honeypot module is implemented to capture and analyze the specific attack behaviors of malicious actors. By integrating honeynet probes with real services and employing attack behavior analysis alongside internet protocol (IP) address redirection techniques, the GH-MTD system achieves a defense response that is both cost efficient and highly effective. Empirical evaluation reveals a 5.5-fold enhancement in attack diversion probability through benchmarking with service-oriented MTD architectures, while the capture rate surpasses that of conventional honeypots by 3.4 times. Particularly against real attackers, GH-MTD exhibits 5.6 times more captured packets and extends the time consumed by attackers by 1.5 times over that of standalone honeypots. In our experiments, we evaluate the architecture's performance against various attack methods, including automated scripts, manual attacks, and assaults by high-level penetration testers. The results demonstrate that the GH-MTD architecture performs exceptionally well, particularly in mitigating and countering advanced, sophisticated attacks, thereby demonstrating its effectiveness in modern network defense strategies.

Heterogeneous Computing Systems (HCSs) have developed rapidly due to their high performance and low cost, and have been adopted by more and more applications. Energy consumption, reliability, and schedule length are the core issues of HCSs. Due to the negative correlation between frequency and reliability, DVFS-supported HCSs requires high energy consumption and a long schedule length to obtain high reliability, which resulting in performance degradation. In this paper, we focus on the reliability and performance-aware scheduling for energy-constrained parallel applications on HCSs. First, we design an energy pre-allocation mechanism based on Energy Demand Rate (EDR) to pre-allocate energy reasonably. Second, we propose an EDR-aware Maximizing Reliability of Energy-Constrained parallel applications (EMREC) scheduling algorithm. Third, considering that maximize reliability will cause the schedule length to be too long and unacceptable, we further highlight the concept of Reliability Performance Ratio (RPR). Finally, we propose a Maximizing RPR with Energy-Constrained parallel applications (MRPEC) scheduling algorithm, which enables parallel applications have a smaller schedule length while with high reliability. Extensive experimental results in real-world and randomly generated applications show the effectiveness of the proposed algorithms under different conditions.

As one of the most recognized models in machine learning, the conditional random fields (CRF) has been widely used in many applications. As the parameter estimation of CRF is highly time-consuming, how to improve the performance of CRF has received significant attention, in particular in the big data environment. To deal with large-scale data, CPU-based or GPU-based parallelization solutions have been proposed to improve performance. However, the problem is an ongoing one. In this paper, we focus on the big data environment and propose a distributed CRF on a heterogeneous CPU-GPU cluster called DHCRF. Our approach differs from previous work. Specifically, it leverages a three-stage heterogeneous Map and Reduce operation to improve the performance, making full use of CPU-GPU collaborative computing capabilities in a big data environment. Furthermore, by combining elastic data partition and intermediate results multiplexing method, the distributed CRF is optimized. Elastic data partition is performed to keep the load balanced, and the intermediate results multiplexing method is adopted to reduce data communication. Experimental results show that the DHCRF outperforms the baseline CRF algorithm and the CPU-based parallel CRF algorithm with notable performance improvement while maintaining competitive correctness at the same time.