ILIDViz: An Incremental Learning-Based Visual Analysis System for Network Anomaly Detection

Abstract

Background

With the development of information technology, network traffic logs mixed with various kinds of cyber-attacks have grown explosively. Traditional intrusion detection systems (IDS) have limited ability to discover new inconstant patterns and identify malicious traffic traces in real-time. It is urgent to implement more effective intrusion detection technologies to protect computer security.

Methods

In this paper, we design a hybrid IDS, combining our incremental learning model (KAN-SOINN) and active learning, to learn new log patterns and detect various network anomalies in real-time.

Results & Conclusions

The experimental results on the NSLKDD dataset show that the KAN-SOINN can be improved continuously and detect malicious logs more effectively. Meanwhile, the comparative experiments prove that using a hybrid query strategy in active learning can improve the model learning efficiency.