With the popularity of the Internet and the rapid development of information technology, it has become critical to accurately predict the security situation of the network environment and timely keep watch on those potentially dangerous attacks according to the security situation prediction. Therefore, in this paper, we propose a novel network security situation prediction model based on temporal deep learning. We combine the attention mechanism with recurrent networks to learn the historical time series network data's hidden features. Then a predictive layer is applied to analyze the hidden features and predict the network security situation. Our experiment results show that our proposed model is significantly better than ARIMA, DNN, and other comparative models, demonstrating the effectiveness of our proposed model in network security situation prediction.
The computer network has been widely used in various industries of society, and network security has received unprecedented attention. Network intrusion detection technology is the critical technologies, which can maintain network security. However, the traditional rule-based intrusion detection method has some shortcomings, such as relying on manual intervention, and it is difficult to update the rule database in real-time. Therefore, in this paper, we propose a novel network intrusion detection model based on deep attention neural network. In particular, we combine the LSTM, multi-layer perception and the attention mechanism in an end-to-end model in order to extract features automatically by deep learning technologies. Finally, we conduct extensive experiments on the KDD99 and NSL-KDD dataset, and the results demonstrate the effectiveness of our proposed approach.
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