relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2361/
title: POCAD: a Novel Payload-based One-Class Classifier for Anomaly Detection
creator: Nguyen, Xuan Nam
creator: Nguyen, Dai Tho
creator: Vu, Hai Long
subject: Information Technology (IT)
description: 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].
date: 2016
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2361/1/POCAD_CameraReady.pdf
identifier:   Nguyen, Xuan Nam and Nguyen, Dai Tho and Vu, Hai Long  (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.     
relation: http://www.nafosted-nics.org