期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：5
出版社：IJCSI Press
摘要：False alarm rate and detection accuracy are still challenging issues that are not completely solved yet in the field of Anomaly based Intrusion Detection System (AIDS). The reasons behind these issues vary according to the algorithm and the dataset used to train the IDS. Consequently, dealing with high dimensional data requires an efficient data reduction technique that considerably reduces the dimensionality without any substantial loss in the important features. However, the excessive reduction of features will lead to model some intrusive patterns similarly as normal ones. Indeed, this will result in misclassifications that will increase false negative rate, which degrades the accuracy of detection. This paper concludes many clustering techniques that were previously proposed to solve the inherent IDS problems. Where, the clustering techniques involved in three general aspects namely: data preprocessing, anomaly detection, and data projection/alarm filtering. Eventually, recommendations for future researches followed by the conclusion are depicted at the end of this paper.
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