Network security situation assessment based on dual attention mechanism and HHO-ResNeXt. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Zhao, Dongmei; Ji, Guoqing; Zeng, Shuiguang
• Is Part Of: Connection science. Volume 35:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Efficient channel attention module -- contextual transformer block -- ResNeXt -- Harris hawks optimisation -- network security situation assessment; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2023.2174080
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : The traditional convolutional neural network (CNN) has a limited receptive field and cannot accurately identify the importance of each channel, making it difficult to solve increasingly complex network security problems. To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. The model can accurately obtain the importance of each channel to assign weights to each channel while making full use of the rich contexts among neighbour keys, effectively enhancing the convolutional neural network. Furthermore, this paper calculates the network security situation value (NSSV) of the adopted datasets based on attack impact. Lastly, experiments on two cybersecurity datasets show that the comprehensive performance of the model on the three indicators of accuracy, precision and F-scores, as well as network security situation assessment, are superior to other models.