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A Denial-of-Service (DoS) attack is an intrusive attempt, which aims to force a designated resource (e.g., network bandwidth, processor time or memory) to be unavailable to its intended users. This attack is launched either by deliberately exploiting system vulnerabilities of a victim (e.g., a host, a router, or an entire network) or by flooding a victim with large volume of useless network traffic. Thus detecting known and unknown DoS attacks by classification effectively by learning the patterns of legitimate network traffic are more important.
Classification algorithms have been traditionally designed to simultaneously reduce errors caused by bias as well by variance. However, low generalization error becomes extremely crucial to getting tangible classification solutions, and even slight overfitting causes serious consequences in the test results.
In such situations, classifiers with low Vapnik-Chervonenkis (VC) dimension can bring out positive differences due to two main advantages:
In the implementation Multivariate Correlation Analysis (MCA) is used for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. A Triangle-Area-Map-based (TAM) technique is proposed to enhance and to speed up the process of MCA.
Finally, for data classification, Majority Vote Point (MVP) classifier is used which can achieve a lower generalization error (statistically) than that of linear classifiers, on account of a lower VC dimension. The proposed detection system is evaluated using KDD dataset, and the performance of MVP classifier with SVM and RBF classifier of proposed detection system are examined. Index Terms—Denial-of-Service attack, multivariate correlations, triangle area,majority vote point (MVP) classifier, VapnikChervonenkis (VC) dimension.
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