Open AccessNetwork security situation prediction method based on strengthened LSTM neural network
Author(s) -
Zhaowei Dong,
Xiaoyu Su,
Liting Sun,
Kuikui Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1856/1/012056
Subject(s) - sigmoid function , computer science , cuckoo search , artificial neural network , convergence (economics) , activation function , artificial intelligence , network security , data mining , set (abstract data type) , rate of convergence , function (biology) , machine learning , algorithm , key (lock) , computer security , particle swarm optimization , evolutionary biology , economics , biology , programming language , economic growth
As an important part of network security situation awareness, network security situation prediction describes the dynamic changes of security situation over time, and predicts future situation values based on historical situation values. In order to improve the accuracy of network security situation prediction, a long- and short-term memory network security situation prediction model based on the Sigmoid weighted reinforcement mechanism is proposed. Firstly, LSTM neural network is used to mine the temporal correlation of network security situation data. Sigmoid weighted linear element is introduced to deal with the gradient problem in the back propagation, and the input value is multiplied by Sigmoid activation function, so as to strengthen the structure of LSTM neural network and improve the accuracy of prediction.Then, the cuckoo search algorithm was used to optimize the super parameters to improve the training time. Finally, the public data set CICIDS2017 was used to verify the model. The simulation experiment results show that the model has a faster convergence rate and smaller errors.