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POCAD: a Novel Payload-based One-Class Classifier for Anomaly Detection

Xuan Nam Nguyen and Dai Tho Nguyen and Hai Long Vu (2016) POCAD: a Novel Payload-based One-Class Classifier for Anomaly Detection. In: 2016 3rd National Foundation for Science and Technology Development (NAFOSTED) Conference on Information and Computer Science (NICS), September 14-16, 2016, Danang City, Vietnam.

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Official URL: http://www.nafosted-nics.org

Abstract

In this paper, we propose a novel Payload-based One-class Classifier for Anomaly Detection called POCAD, which combines a generalized 2v-gram feature extractor and a one-class SVM classifier to effectively detect network intrusion attacks. We extensively evaluate POCAD with real-world datasets of HTTP-based attacks. Our experiment results show that POCAD can quickly detect malicious payload and achieves a high detection rate as well as a low false positive rate. The experiment results also show that POCAD outperforms state of the art payload-based detection schemes such as McPAD [8] and PAYL [5].

Item Type:Conference or Workshop Item (Paper)
Subjects:Information Technology (IT)
Divisions:Faculty of Information Technology (FIT)
ID Code:2361
Deposited By: Dr. Dai Tho Nguyen
Deposited On:29 Dec 2016 08:24
Last Modified:12 Jan 2017 16:15

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