Network Intrusion Detection Based on Deep Belief Network Broad Equalization Learning System

Abstract

Network intrusion detection systems are an important defense technology to guarantee information security and protect a network from attacks. In recent years, the broad learning system has attracted much attention and has been introduced into intrusion detection systems with some success. However, since the traditional broad learning system is a simple linear structure, when dealing with imbalanced datasets, it often ignores the feature learning of minority class samples, leading to a poorer recognition rate of minority class samples. Secondly, the high dimensionality and redundant features in intrusion detection datasets also seriously affect the training time and detection performance of the traditional broad learning system. To address the above problems, we propose a deep belief network broad equalization learning system. The model fully learns the large-scale high-dimensional dataset via a deep belief network and represents it as an optimal low-dimensional dataset, and then introduces the equalization loss v2 reweighing idea into the broad learning system and learns to classify the low-dimensional dataset via a broad equalization learning system. The model was experimentally tested using the CICIDS2017 dataset and fully validated using the CICIDS2018 dataset. Compared with other algorithms in the same field, the model shortens the training time and has a high detection rate and a low false alarm rate.