Security Situation Prediction of Network Based on Lstm Neural Network

Chen, Liqiong; Fan, Guoqing; Guo, Kun; Zhao, Junyan

doi:10.1007/978-3-030-79478-1_12

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Based on machine learning (ML) [6], Xing et al and Wang Jian et al used a support vector machine (SVM) for situation prediction [7,8], which has a fast response time and a small model memory but a relatively low prediction accuracy. Based on deep learning (DL) [6], Wei et al used gated recurrent unit (GRU) for situation prediction [9]; Chen et al used long short-term memory (LSTM) for situation prediction [10]; and Guosheng et al used backpropagation (BP) neural network for situation prediction [11]. Situation prediction using DL is relatively more complex and computationally intensive; however, it has higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

Yao¹,

Yang²,

Yang³

et al. 2022

Mathematical Problems in Engineering

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The rapid development of information technology has brought much convenience to human life, but more network threats have also come one after another. Network security situation prediction technology is an effective means to protect against network threats. Currently, the network environment is characterized by high data traffic and complex features, making it difficult to maintain the accuracy of the situation prediction. In this study, a network security situation prediction model based on attention mechanism (AM) improved temporal convolutional network (ATCN) combined with bidirectional long short-term memory (BiDLSTM) network is proposed. The TCN is improved by AM to extract the input temporal features, which has a more stable feature extraction capability compared with the traditional TCN and BiDLSTM, which is more capable of processing temporal data, and is used to perform the situation prediction. Finally, by validating on a real network traffic dataset, the proposed method has better performance on multiple loss functions and has more accurate and stable prediction results than TCN, BiDLSTM, TCN-LSTM, and other time-series prediction methods.

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