Browse Publications Technical Papers 12-07-03-0022
2024-04-17

Research on Network Security Situation Prediction Algorithm Combining Intuitionistic Fuzzy Sets and Deep Neural Networks 12-07-03-0022

This also appears in SAE International Journal of Connected and Automated Vehicles-V133-12EJ

The expansion of the internet has made everyone’s personal and professional lives more transparent. There are network security issues because people like sharing resources under the right conditions. Academics have demonstrated significant interest in situation awareness, which includes situation prediction, situation appraisal, and event detection, rather than focusing on the security of a single device in the network. Multi-stage attack forecasting and security situation awareness are two significant issues for network supervisors because the future usually is unknown. Hence, this study suggests combined intuitionistic fuzzy sets and deep neural network (CIFS-DNN) for network security situation prediction. The goal is to provide network administrators with a resource they can use as a point of reference while they formulate and carry out preventive actions in the event of a network assault. The job requires differentiating between the event of an assault and a typical instance, as well as differentiating between the various sorts of attacks and a typical case. In this article, we present a model that can more accurately and effectively forecast network security scenarios, and our experiments bear this out. The results show that the proposed technique is successful and exact in predicting network security issues. The suggested CIFS-DNN approach has a low delay rate of 10%, a low latency rate of 20%, a low error rate of 25%, a high prediction ratio of 98.6%, a high security rate of 98.3%, a high accuracy ratio of 99.6%, and a high efficiency ratio of 93.9%

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