文章基本信息

标题：REDUCING FALSE ALARM USING HYBRID INTRUSION DETECTION BASED ON X-MEANS CLUSTERING AND RANDOM FOREST CLASSIFICATION
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作者：SUNDUS JUMA ; ZAITON MUDA ; WARUSIA YASSIN 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：68
期号：2
出版社：Journal of Theoretical and Applied
摘要：In recent times, Intrusion Detection systems (IDSs) incarnate the high network security. Anomaly-based intrusion detection techniques, that utilize algorithms of machine learning, have the capability to recognize unpredicted malicious. Unluckily, an essential provocation of this method is to maximize accuracy, detection whereas minimize false alarm rate. This paper proposed a hybrid machine learning approach based on X-Means clustering and Random Forest classification called XM-RF in order to aforementioned drawbacks. X-Means clustering is utilized to gather whole data into congruent cluster based on their behaviour whereas Random Forest classifier is utilized to rearrange the misclassified clustered data to apropos group. The ISCX 2012 Intrusion Detection Evaluation is used as a model dataset. The experimental result pose that the proposed approach obtains better than other techniques, with the accuracy, detection and false alarm rates of 99.96%, 99.99%, and 0.2%, respectively.
关键词：Intrusion Detection System; Anomaly-based Intrusion Detection; Machine Learning; X-Means; Random Forest.