Abstract
Using machine learning algorithm to build detection model of an intrusion detection system (IDS) to detect abnormal behaviors is an effective way that can be found in several studies; however, some different abnormal behaviors have similar characteristics that are quite difficult to be distinguished by using a single one detection model. To effectively identify such abnormal behaviors, the proposed method will construct a certain number of classifiers for different abnormal behaviors as a hierarchical and ensemble classification (detection) model. The proposed IDS will also adopt the domain adaptation method to remove the irrelevant augmented data because some of them may be assigned with incorrect labels during the augmentation process. Experimental results show that the proposed method can outperform other classification methods in terms of accuracy and recall such as machine learning, ensemble learning, and deep learning methods. It is shown that the proposed method can provide a promising design to detect different types of malicious intrusions.
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Acknowledgment
This research was partially supported by the National Science and Technology Council (NSTC) of Taiwan, R.O.C., under the grant numbers NSTC-111-2222-E-110-006-MY3, NSTC-112-2628-E-110-001-MY3, and NSTC-112-2634-F-110-001-MBK.
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Wang, JP., Wang, TL., Wu, YH., Tsai, CW. (2024). An Effective Ensemble Classification Algorithm for Intrusion Detection System. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2144. Springer, Singapore. https://doi.org/10.1007/978-981-97-5937-8_5
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