关键词: 网络入侵检测, 深度学习, 流量异常检测, 流量预测, 神经网络

Abstract:

The Network Intrusion Detection System（NIDS） can detect network security events in the current system. However, due to its high false alarm rate and the delay in identifying security events, it is not possible to provide a high-precision early warning function for network security events in advance, which seriously restricts the practical application and future development of NIDS. A network traffic anomaly prediction method based on deep learning is proposed. This method proposes a prediction model that combines long-short-term memory network and convolutional neural network of deep learning algorithms, which can learn the spatiotemporal characteristics of network traffic data and realizes the prediction of changes in traffic characteristics and network security event classification in the next period. It provides method analysis for NIDS to realize the early warning function of network security events. The experiment uses the designed neural network framework to train and test the intrusion detection system traffic dataset CICIDS2017. Under this method, the false alarm rate of traffic classification decreases to 0.26%, the overall accuracy rate reaches 99.57% and the best effect of traffic feature prediction model R2 reaches 0.762.

Key words: network intrusion detection, deep learning, traffic anomaly detection, traffic forecast, neural network