A novel cyber intrusion detection model based on improved hybrid sampling

Abstract

The class imbalance of samples of network traffic will cause the poor classification performance of intrusion detection models based on machine learning. To solve this problem, this paper researches sampling algorithm and deep learning for intrusion detection in imbalanced network traffic. This paper proposes a deep recurrent neural network (delayed long short-term memory (DLSTM)) intrusion detection model based on the balanced samples. First, an improved hybrid sampling (IHS) method based on chaotic particle swarm optimization (CPSO) algorithm is proposed as the sampling algorithm to balance the imbalanced samples. Next, a DLSTM with long short-term memory (LSTM) function is proposed to realize high-precision classification of intrusion behaviours. Finally, the method is validated on the standard network traffic dataset. The experimental results show that the DLSTM intrusion detection model based on the IHS method outperforms other comparative models at accuracy. The model is available to the computer network information security defence system.