Abstract
As the viruses and Trojans become more and more rampant and ingenious, the Intrusion Detection technology is a new security technology which is considered to be the second safe gate after the fire wall. This thesis brings forth new ideas of Intrusion Detection System based on support vector machine active learning and data fusion which is completely different from traditional IDSs. This IDS model has an improved algorithm in its incident analysor part that presents some advantages of finding details of concrete attack detecting efficiency and being convenient to update because of the dependence of each classifiers.
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Zhao, M., Zhai, J., He, Z. (2010). Intrusion Detection System Based on Support Vector Machine Active Learning and Data Fusion. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_28
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DOI: https://doi.org/10.1007/978-3-642-16493-4_28
Publisher Name: Springer, Berlin, Heidelberg
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