Publish/subscribe systems on the traditional Internet suffer from poor scalability and high delay in the face of the Internet of Things (IoT) environment. Being customizable, the paradigm of software-defined networking (SDN) provides a chance to establish an IoT-specific network. In this paper, we propose an SDN-based publish/subscribe system named SDNPS, which can construct and fine-tune topic-connected overlays for the sake of disseminating events efficiently and non-redundantly based on a global topology overview. It organizes topics as a Huffman-like topic tree and codes them into binary strings so that filtering and forwarding events can be operated directly on SDN-configurable switches, which helps to reduce end-to-end latency. This hierarchical organization form of topic tree makes it possible to incrementally construct and store overlays, which contribute to reducing the time and space complexity of routing computation. More specifically, it achieves a better tradeoff between load-balancing of the overall optimization objective and the minimal forwarding cost of per-topic overlay.

Users can invoke various composite services in Edge Computing (EC) with the development of Service Computing. Generally, users cannot invoke the optimal composite service due to the migration of component service or failure in the edge device. In this study, a Composite Service Provisioning mechanism in EC (CSP-EC) is proposed. It starts when the location of component service changes and terminates when the optimal composite service is obtained. CSP-EC employs dynamic optimization process to meet the real-time requirements. It first caches optimized intermediate solutions from the first users, which can be reused by other users to get the final optimal solution. Moreover, a multipopulation Estimation of Distribution Algorithm is used to reduce the probability of falling into a local optimum, and the roulette mechanism is used to accelerate the optimization process. Extensive simulations are conducted based on the real-world dataset. The results show that the proposed mechanism achieves higher quality, better stability, and shorter execution time compared with other schemes.

Vehicular edge computing (VEC) represents a concrete application of mobile edge computing (MEC) in the field of intelligent transportation, with task offloading serving as one of its core components. The design of efficient task offloading strategies poses significant challenges due to the dynamic network topology, stringent low-latency requirements, and massive data processing demands. This paper proposes a digital twin (DT)-assisted intelligent task offloading approach, which establishes a dynamic interaction and mapping between the virtual and physical worlds to enable real-time monitoring of VEC network states, thereby optimizing offloading decisions. First, to meet diverse user service requirements, an optimization model is formulated with the objective of minimizing task processing latency and energy consumption. Next, a gravity model-based vehicle clustering algorithm is integrated with digital twin technology to find the optimal offloading space and ensure link stability among vehicles within aggregated clusters. Furthermore, to minimize overall system costs, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to train the offloading policy, enabling automatic optimization of both latency and energy consumption. consumption. Finally, a feedback mechanism is introduced to dynamically adjust parameters and enhance the robustness of the clustering process. Simulation results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task completion cost, energy consumption, delay, and success rate, thereby validating its potential and superior performance in dynamic vehicular network environments.

In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection.

By deploying resources in the vicinity of users, edge caching can substantially reduce the latency for users to retrieve content and relieve the pressure on the backbone network. Due to the capacity limitation of caching and the dynamic nature of user requests, how to allocate caching resources reasonably must be considered. Some edge caching studies improve network performance by predicting content popularity and actively caching the most popular content, thereby ignoring the privacy and security issues caused by the need to collect user information at the central unit. To this end, a collaborative caching strategy based on federated learning is proposed. First, federated learning is used to make distributed predictions of the preferences of users in the nodes to develop an effective content caching policy. Then, the problem of allocating caching resources to optimize the cost of video providers is formulated as a Markov decision process, and a reinforcement learning method is used to optimize the caching decisions. Compared with several basic caching strategies in terms of cache hit rate, transmission delay, and cost, the simulation results show that the proposed content caching strategy reduces the cost of video providers, and has higher cache hit rate and lower average transmission delay.