Real-time traffic intrusion detection based on CNN-LSTM deep neural networks

Abstract

As Internet technology is developed and applied, the problems of large amounts of real-time traffic data and many unknown attacks are becoming increasingly serious, and intrusion detection systems have increased in efficiency and effectiveness. In this paper, a real-time traffic intrusion detection method based on Inception-LSTM deep neural network combining CNN and LSTM is proposed for improving label-based intrusion detection performance. Network traffic records are converted into 2D gray scale graphs. It extracts network traffic features using image processing techniques with high generalization ability. Experimental validation is performed on the publicly available CIC-IDS-2017 dataset, and the results show that the proposed Inception-LSTM neural network improves the detection accuracy and F1-score by 0.5% and 0.7%, respectively; the results of the comparison between the detection done on real-time captured traffic data and the network security devices show that the method is effective and feasible.