Encrypted traffic classification has become increasingly important in network security. To address the difficulty of existing architectures in collaboratively modeling spatio-temporal features, we propose BiST-Mamba, a novel dual-branch spatio-temporal Mamba network that enables simultaneous representation of spatio-temporal features. To the best of our knowledge, this is the first work to introduce VMamba into encrypted traffic classification. Preliminary experiments on a small-scale dataset show that our accuracy and F1 scores reach 94.13% and 93.41%, respectively. The method achieves promising classification performance, demonstrating the potential of the model for effective spatio-temporal modeling.

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

The key to the problem of node coverage in wireless sensor networks (WSN) is to deploy a limited number of sensors to achieve maximum coverage. This paper studies the hybrid strategies of multiple evolutionary algorithms, and applies them to the problem of WSN node coverage. We first proposed the hybrid algorithm SFLA‐WOA (SWOA) based on Shuffled Frog Leaping Algorithm (SFLA) and Whale Optimization Algorithm (WOA). The SWOA algorithm combines the advantages of SFLA and WOA; that is, it retains the unique evolution model of WOA and also has the excellent co‐evolution capability of SFLA. Secondly, using the mutation, crossover and selection operations of the differential evolution (DE) algorithm to further optimize this hybrid algorithm, the SWOA‐based SFLA‐WOA‐DE (SWOAD) algorithm is proposed. In addition, the performance of SWOA and SWOAD has been tested by 30 benchmark functions in the CEC 2017 test set. Experimental results show that the optimization effects of these two algorithms are very outstanding. Finally, the simulation results show that the optimization algorithm proposed in this paper has a good effect on improving the signal coverage of WSN under the actual three‐dimensional terrain.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.