Abstract
Network attack behavior detection using deep learning is an important research topic in the field of network security. Currently, there are still many challenges in detecting multi-class imbalanced abnormal traffic data. This paper proposed a new intrusion detection network based on deep learning, named parallel cross convolutional neural network (PCCN), to improve the detection performance of imbalanced abnormal flows. By fusing the flow features learned from the two branch convolutional neural networks (CNN), PCCN can better learn the flow features with fewer samples, to improve the detection results of the imbalanced abnormal flows. We proposed an improved feature extraction method of the original flow to extract multi-class flow features at the same time. The proposed algorithm not only reduces the number of useless elements for network learning, but also accelerates network convergence. In addition, we proposed four improved versions of the PCCN network structure to meet the real-time requirements of network intrusion detection in the current big data computing. These networks can achieve almost the same detection results as the PCCN, but greatly reduce the detection time of data. Through the analysis of high-order evaluation metrics, the proposed PCCN algorithm is significantly better than the traditional machine learning algorithms. Compared with the current hierarchical network model, PCCN can also achieve better performance in term of overall accuracy.
Highlights
In recent years, with the rapid development of the Internet and the increase in the number of users, the network security and management have suffered great threats
We propose a new parallel cross convolutional neural network(PCCN) based on feature fusion, which uses the original features of flows to improve the detection results of imbalanced abnormal traffic data
Since the original flow feature extraction method introduces a large number of 0 elements that are useless for learning, we propose an improved original flow feature extraction algorithm
Summary
With the rapid development of the Internet and the increase in the number of users, the network security and management have suffered great threats. Researchers will extract various fields from the flow data to effectively detect traffic data with abnormal behaviors. For the detection of imbalanced abnormal traffic data, manually designed features cannot achieve a better detection effect for categories with small data volume. We propose a new parallel cross convolutional neural network(PCCN) based on feature fusion, which uses the original features of flows to improve the detection results of imbalanced abnormal traffic data. The experimental results show that the proposed method can effectively improve the detection performance of imbalanced abnormal flow data and only cause a little detection delay. (1) We propose a new network model, named PCCN, to improve the detection performance of highly imbalanced abnormal flow through feature fusion.
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