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

Abstract

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.

Network Security Situation Prediction Model Based on VMD Decomposition and DWOA Optimized BiGRU-ATTN Neural Network

“…Zhu et al addressed the inefficiency of traditional neural networks by optimizing the LSTM using an improved Nadam algorithm, which resulted in increased training speed and prediction accuracy [34]. Yao et al proposed a temporal convolutional network (TCN) and bidirectional (BILSTM) combination model to predict network security situations [35]. The TCN was used to extract the time series features.…”

“…ATCN-BiDLSTM [35]: A network security situational prediction model is established, combining an attention mechanism-enhanced temporal convolutional network (ATCN) with a bidirectional long short-term memory (BiDLSTM) network.…”