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Applied Research on Data Mining Algorithm in Network Intrusion Detection

机译:数据挖掘算法在网络入侵检测中的应用研究

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Intrusion detection is one of network security area of technology main research directions. Data mining technology was applied to network intrusion detection system (NIDS), may automatically discover the new pattern from the massive network data, to reduce the workload of the manual compilation intrusion behavior patterns and normal behavior patterns. This article reviewed the current intrusion detection technology and the data mining technology briefly. Focus on data mining algorithm in anomaly detection and misuse detection of specific applications. For misuse detection, the main study the classification algorithm; for anomaly detection, the main study the pattern comparison and the cluster algorithm. In pattern comparison to analysis deeply the association rules and sequence rules . Finally, has analysed the difficulties which the current data mining algorithm in intrusion detection applications faced at present, and has indicated the next research direction.
机译:入侵检测是网络安全领域技术的主要研究方向之一。数据挖掘技术被应用于网络入侵检测系统(NIDS),可以从大量的网络数据中自动发现新的模式,以减少手动编译入侵行为模式和正常行为模式的工作量。本文简要回顾了当前的入侵检测技术和数据挖掘技术。专注于数据挖掘算法在异常检测和滥用检测中的特定应用。对于滥用检测,主要研究分类算法;对于异常检测,主要研究模式比较和聚类算法。在模式比较中对关联规则和顺序规则进行了深入的分析。最后,分析了当前数据挖掘算法在入侵检测应用中目前面临的困难,并指出了下一步的研究方向。

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