Abstract
Anomaly detection is very crucial in an intrusion detection task since it has capability to discover new types of attacks. The major challenges of anomaly detection are how to maximize the accuracy while maintaining low positive rate. In this paper, we propose new approach on anomaly detection using multi-level classifier ensembles. We employ an ensemble learner as a base classifier of ensemble rather than a single classifier algorithm. We run several experiments to choose the best combination of two-level classifier ensemble model. From our experimental result, it is revealed that the performance of our proposed approach yields satisfactory results over classical classifier ensembles and single classifiers.
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Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) No. NRF-2014R1A2A1A11052981 and Korean Government Scholarship Program (KGSP) for Graduate 2013–2018.
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Tama, B.A., Rhee, KH. (2017). A Novel Anomaly Detection Method in Wireless Network Using Multi-level Classifier Ensembles. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_73
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DOI: https://doi.org/10.1007/978-981-10-5041-1_73
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