Abstract
Along with recent technological advances more and more new threats and advanced cyber-attacks appear unexpectedly. Developing methods which allow for identification and defense against such unknown threats is of great importance. In this paper we propose new ensemble method (which improves over the known cross-feature analysis, CFA, technique) allowing solving anomaly detection problem in semi-supervised settings using well established supervised learning algorithms. Theoretical correctness of the proposed method is demonstrated. Empirical evaluation results on Android malware datasets demonstrate effectiveness of the proposed approach and its superiority against the original CFA detection method.
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Tenenboim-Chekina, L., Rokach, L., Shapira, B. (2013). Ensemble of Feature Chains for Anomaly Detection. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_26
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DOI: https://doi.org/10.1007/978-3-642-38067-9_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38066-2
Online ISBN: 978-3-642-38067-9
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