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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51795


    Title: A triangle area based nearest neighbors approach to intrusion detection
    Authors: Tsai,CF;Lin,CY
    Contributors: 資訊管理學系
    Date: 2010
    Issue Date: 2012-03-27 19:06:22 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Intrusion detection is a necessary step to identify unusual access or attacks to secure internal networks. In general, intrusion detection can be approached by machine learning techniques. In literature, advanced techniques by hybrid learning or ensemble methods have been considered, and related work has shown that they are superior to the models using single machine learning techniques. This paper proposes a hybrid learning model based on the triangle area based nearest neighbors (TANN) in order to detect attacks more effectively. In TANN, the k-means clustering is firstly used to obtain cluster centers corresponding to the attack classes, respectively. Then, the triangle area by two cluster centers with one data from the given dataset is calculated and formed a new feature signature of the data. Finally, the k-NN classifier is used to classify similar attacks based on the new feature represented by triangle areas. By using KDD-Cup '99 as the simulation dataset, the experimental results show that TANN can effectively detect intrusion attacks and provide higher accuracy and detection rates, and the lower false alarm rate than three baseline models based on support vector machines, k-NN, and the hybrid centroid-based classification model by combining k-means and k-NN. (C) 2009 Elsevier Ltd. All rights reserved.
    Relation: PATTERN RECOGNITION
    Appears in Collections:[資訊管理學系] 期刊論文

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