Network posture malicious traffic prediction evaluation based on LSTM recurrent neural network

Guosheng Tan

doi:10.1117/12.2639374

23 May 2022 Network posture malicious traffic prediction evaluation based on LSTM recurrent neural network

Guosheng Tan

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225422 (2022) https://doi.org/10.1117/12.2639374
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

With the rapid development of mobile Internet and the continuous expansion of network scale, the network security situation has become increasingly severe, and the endless network security threats have put forward higher requirements for network security performance. In this paper, we investigate this issue from two perspectives of situation assessment and situation prediction, and the main research work includes, for the traditional BP neural network situation assessment model, the network attack assessment only considers the attack effect, and the model convergence speed is slow due to the inaccurate parameter initialization settings, and the assessment efficiency is poor. An LSTM-based method for detecting malicious network traffic in an encrypted environment is proposed, while the network posture assessment based on the optimized BP neural network for the attack impact index system is carried out to achieve real-time monitoring and analysis of the network security status.

Citation Download Citation

Guosheng Tan "Network posture malicious traffic prediction evaluation based on LSTM recurrent neural network", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225422 (23 May 2022); https://doi.org/10.1117/12.2639374

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