Network intrusion detection system with incremental active learning

Bedir Tüzün, Münteha Nur
While Internet usage has increased every year, it has gained momentum in recent years with the global pandemic. Increasing Internet usage has brought increasing cyber threats. Intrusion detection systems have become more important than ever. The performance of these systems is directly proportional to their adaptiveness to the rapid changes in attack types. However, desired performance cannot always be achieved due to the lack of labeled data on newly developed attacks and the difficulty of incremental learning with machine learning methods. In this study, we proposed a network intrusion detection system using active learning methods for class incremental learning, which can adapt to the dynamic environment and provide high performance with less labeled data. Experiment results show that the proposed method requires fewer labeled training data instances and learns new types of attacks incrementally.


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Citation Formats
M. N. Bedir Tüzün, “Network intrusion detection system with incremental active learning,” M.S. - Master of Science, Middle East Technical University, 2022.