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

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