Abstract
As the Internet spreads to each corner of the world, computers are exposed to miscellaneous intrusions from the World Wide Web. Thus, we need effective intrusion detection systems to protect our computers from the intrusions. Traditional instance-based learning methods can onlyb e used to detect known intrusions since these methods classifyinsta nces based on what theyha ve learned. Theyrarely detect new intrusions since these intrusion classes has not been learned before. We expect an unsupervised algorithm to be able to detect new intrusions as well as known intrusions.
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Guan, Y., Ghorbani, A.A., Belacel, N. (2003). An Unsupervised Clustering Algorithm for Intrusion Detection. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_60
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DOI: https://doi.org/10.1007/3-540-44886-1_60
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