Attention-based deep learning for network intrusion detection

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.