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An Unsupervised Clustering Algorithm for Intrusion Detection

机译:一种无监督的入侵检测聚类算法

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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 only be used to detect known intrusions since these methods classify instances based on what they have learned. They rarely 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.
机译:随着互联网传播到世界各个角落,计算机暴露于万维网的杂项入侵。因此,我们需要有效的入侵检测系统来保护我们的计算机免受入侵。基于传统的基于实例的学习方法只能用于检测已知入侵,因为这些方法基于所学到的内容对实例进行分类。它们很少检测新的入侵,因为这些入侵课程尚未在之前学习。我们预计无监督算法能够检测新的入侵以及已知入侵。

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