Effectiveness of Focal Loss for Minority Classification in Network Intrusion Detection Systems

Abstract

As the rapid development of information and communication technology systems offers limitless access to data, the risk of malicious violations increases. A network intrusion detection system (NIDS) is used to prevent violations, and several algorithms, such as shallow machine learning and deep neural network (DNN), have previously been explored. However, intrusion detection with imbalanced data has usually been neglected. In this paper, a cost-sensitive neural network based on focal loss, called the focal loss network intrusion detection system (FL-NIDS), is proposed to overcome the imbalanced data problem. FL-NIDS was applied using DNN and convolutional neural network (CNN) to evaluate three benchmark intrusion detection datasets that suffer from imbalanced distributions: NSL-KDD, UNSW-NB15, and Bot-IoT. The results showed that the proposed algorithm using FL-NIDS in DNN and CNN architecture increased the detection of intrusions in imbalanced datasets compared to vanilla DNN and CNN in both binary and multiclass classifications.

Keywords

• deep learning
• neural networks
• intrusion detection
• imbalanced datasets
• minority classification