AEC_GAN: Unbalanced Data Processing Decision-Making in Network Attacks Based on ACGAN and Machine Learning

Abstract

Using deep learning and machine learning techniques for network intrusion detection is of great significance for enhancing the defense capability of network security systems. Given the characteristics of generative adversarial networks, such as the approximate consistency of generated samples with the input data distribution but with a random distribution within a certain bounded interval, and in response to the problem of insufficient classification performance and detection omission caused by the imbalance of different degrees of data categories and quantities in network intrusion traffic, and in light of the fact that the effectiveness of existing classification algorithms based on unbalanced traffic data still has some room for improvement, this paper proposes a network intrusion detection strategy based on auxiliary classifier generative adversarial networks. The data expansion experiments are conducted with the intrusion detection dataset NSL-KDD. The data are classified into twenty-three categories before and after the expansion by binary classification validation. The results show that the expansion of the generated samples for unbalanced network traffic data improve the subsequent recognition effect significantly. Finally, five classification performance index verification experiments are conducted. The results prove that the strategy of this paper performs better in accuracy, precision, recall rate and F-value indexes, and is capable of obtaining a large number of features from limited samples and inferring complete data distribution based on fewer features. The model as a whole has stronger generalization ability and defense effect.

Keywords

• Data augmentation
• generative adversarial networks
• intrusion detection
• multi-class classification
• network security
• unbalanced data