%0 Conference Proceedings
%T Security Situation Prediction of Network Based on Lstm Neural Network
%+ Shanghai Institute of Technology (SIT)
%+ Department of Computer Science and Information Engineering (NCUE)
%+ East China University of Science and Technology
%+ Department of Computer Science and Engineering
%A Chen, Liqiong
%A Fan, Guoqing
%A Guo, Kun
%A Zhao, Junyan
%Z Part 2: AI
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 17th IFIP International Conference on Network and Parallel Computing (NPC)
%C Zhengzhou, China
%Y Xin He
%Y En Shao
%Y Guangming Tan
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-12639
%P 140-144
%8 2020-09-28
%D 2020
%R 10.1007/978-3-030-79478-1_12
%K Network security
%K Parallel processing
%K Situation awareness
%K Network security situation prediction model
%K Lstm network
%Z Computer Science [cs]Conference papers
%X As an emerging technology that blocks network security threats, network security situation prediction is the key to defending against network security threats. In view of the single source of information and the lack of time attributes of the existing methods, we propose an optimal network security situation prediction model based on lstm neural network. We employ the stochastic gradient descent method as the minimum training loss to establish a network security situation prediction model, and give the model implementation algorithm pseudo code to further predict the future network security situation. The simulation experiments based on the data collected from Security Data dataset show that compared with other commonly used time series methods, the prediction accuracy of the model is higher and the overall situation of network security situation is more intuitively reflected, which provides a new solution for network security situation.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03768740/document
%2 https://inria.hal.science/hal-03768740/file/511910_1_En_12_Chapter.pdf
%L hal-03768740
%U https://inria.hal.science/hal-03768740
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-12639