Abstract: Technology development has promoted network construction, but malicious network attacks are still inevitable. To solve the problem that the current network security assessment is not practical and the assessment effect is poor, this study proposes a network security monitoring tool based on situation assessment and prediction to assist network security construction. The framework of the evaluation module is based on a convolution neural network. The initial module is introduced to convert some large convolution cores into small convolution cores in series. This is to reduce the operating cost because building multiple evaluators in series can maximize the retention of characteristic values. This module is the optimized form of the Elman neural network. The delay operator is added to the model to respond to the time property of the network attack. At the same time, the particle swarm optimization algorithm is used to solve the initial weight dependence problem. The research adopts two methods of security situation assessment and situation prediction to carry out model application tests. During the test, the commonly used KDD Cup99 is used as intrusion detection data. The experimental results of the network security situation evaluation module show that the optimization reduces the evaluation error by 3.34%, and the accuracy meets the evaluation requirements. The model is superior to the backpropagation neural network and the standard Elman model. The model proposed in this study achieves better prediction of posture scores from 0.3 to 0.9, which is more stable than the BP neural network. It proves that the model designed by the research can achieve more stable and higher prediction than similar models. It is more practical to obtain better results on the basis of a more stable model architecture and lower implementation costs, which is a meaningful attempt in the wide application of network security.
Keywords: Cybersecurity, Situational assessment, Convolutional neural network, Elman neural network, Performance optimization
Liu Zhang, Department of Electronic Information Engineering, Beihai Vocational College, Beihai, 536000, China
Liu Zhang obtained an engineering degree from Guilin University of Electronic Technology in 2008. She is currently an information system project manager and lecturer in the Department of Electronic Information Engineering of Beihai Vocational College. She has participated in research on multiple projects, including big data analysis and blockchain applications. She has published multiple articles in the journal. Her areas of interest include information security, system development, and machine learning.
Yanyu Liu, Department of Electronic Information Engineering, Beihai Vocational College, Beihai, 536000, China
Yanyu Liu obtained his Master in Computer Application Technology (2010) from Guilin University of Technology, Guilin. Presently, he is working as an Associate Professor in the Department of Electronic Information Engineering, at Beihai Vocational College, Beihai. He has participated in the research of multiple projects, including natural human-computer interaction, software reverse engineering, and Web3D education software. He has published more than 20 articles in journals and conference proceedings. His areas of interest include human-computer interaction, emotion recognition, action recognition, and machine learning.