Abstract
Increasing Internet usage in recent years has correspondingly increased the prevalence of cyber threats, emphasizing the necessity for robust intrusion detection systems (IDS). The efficacy of these systems is crucially dependent on their ability to adapt promptly to the continuously evolving types of cyber-attacks. Nonetheless, achieving the desired performance levels is often hindered by the scarcity of labeled data for newly emerging threats and the complexities associated with implementing incremental learning within machine learning frameworks. In this research, we introduce an IDS that employs active learning techniques for class incremental learning, aimed at adapting to the dynamic cyber security landscape while requiring fewer labeled data instances. The results from our experiments demonstrate that the proposed method significantly reduces the need for labeled training data while effectively incorporating new attack classes incrementally.
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This research has been supported by the TÜBİTAK 3501 Career Development Program under grant number 120E537 and the TÜBA GEBİP Program.
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Tüzün, M.N.B., Angin, P. (2024). Network Intrusion Detection with Incremental Active Learning. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-031-57942-4_33
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DOI: https://doi.org/10.1007/978-3-031-57942-4_33
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