POCAD A novel pay load-based one-class classifier for anomaly detection

Title:	POCAD A novel pay load-based one-class classifier for anomaly detection
Authors:	Nguyen, X.N. Nguyen, D.T. Vu, L.H.
Keywords:	Signal detection Anomaly detection;False positive rates;Feature extractor
Issue Date:	2016
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Citation:	Scopus
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 [4] and PAYL [8].
Description:	NICS 2016 - Proceedings of 2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science 28 October 2016, Article number 7725671, Pages 74-79
URI:	http://ieeexplore.ieee.org/document/7725671/ http://repository.vnu.edu.vn/handle/VNU_123/33648
ISBN:	978-150902098-0
Appears in Collections:	Bài báo của ĐHQGHN trong Scopus