Abstract
With the continuous development of the Internet and a substantial increase in interconnected devices, devices have become more vulnerable to cybersecurity attacks. Deploying Network-based Intrusion Detection Systems (NIDS) can effectively detect malicious traffic. However, a drawback of many existing methods is that once the model is deployed, it is no longer updated, resulting in inadequate predictive capabilities. To make NIDS adapt to new traffic patterns after deployment, some researchers have adopted semi-supervised learning methods, which label the unlabeled data and periodically retrain the model, leading to an improved detection rate. Nevertheless, semi-supervised learning methods often face challenges related to data labeling accuracy, as incorrect labels can negatively impact the model’s performance. To address this issue, we propose a hybrid labeling technique to tackle such problems. Experimental results demonstrate that the proposed hybrid labeling method outperforms a single labeling approach, enhancing model accuracy.
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Hwang, RH., Tsai, TH., Lin, JY. (2023). Intrusion Detection System Using Semi-supervised Learning with Hybrid Labeling Techniques. In: Nghia, P.T., Thai, V.D., Thuy, N.T., Son, L.H., Huynh, VN. (eds) Advances in Information and Communication Technology. ICTA 2023. Lecture Notes in Networks and Systems, vol 847. Springer, Cham. https://doi.org/10.1007/978-3-031-49529-8_9
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DOI: https://doi.org/10.1007/978-3-031-49529-8_9
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