A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks

Anbar, Mohammed; Abdullah, Rosni; Al-Tamimi, Bassam Naji; Hussain, Amir

Abstract

Router advertisement (RA) flooding attack aims to exhaust all node resources, such as CPU and memory, attached to routers on the same link. A biologically inspired machine learning-based approach is proposed in this study to detect RA flooding attacks. The proposed technique exploits information gain ratio (IGR) and principal component analysis (PCA) for feature selection and a support vector machine (SVM)-based predictor model, which can also detect input traffic anomaly. A real benchmark dataset obtained from National Advanced IPv6 Center of Excellence laboratory is used to evaluate the proposed technique. The evaluation process is conducted with two experiments. The first experiment investigates the effect of IGR and PCA feature selection methods to identify the most contributed features for the SVM training model. The second experiment evaluates the capability of SVM to detect RA flooding attacks. The results show that the proposed technique demonstrates excellent detection accuracy and is thus an effective choice for detecting RA flooding attacks. The main contribution of this study is identification of a set of new features that are related to RA flooding attack by utilizing IGR and PCA algorithms. The proposed technique in this paper can effectively detect the presence of RA flooding attack in IPv6 network.

Citation

Anbar, M., Abdullah, R., Al-Tamimi, B. N., & Hussain, A. (2018). A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks. Cognitive Computation, 10(2), 201-214. https://doi.org/10.1007/s12559-017-9519-8

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2017
• Online Publication Date: Oct 23, 2017
• Publication Date: 2018-04
• Deposit Date: Jul 26, 2019
• Publicly Available Date: Jul 26, 2019
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Publisher: BMC
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 2
• Pages: 201-214
• DOI: https://doi.org/10.1007/s12559-017-9519-8
• Keywords: RA flooding attack, Network security, IGR, PCA, SVM, IPv6 security
• Public URL: http://researchrepository.napier.ac.uk/Output/1792106
• Related Public URLs: https://www.storre.stir.ac.uk/handle/1893/26343
• Contract Date: Jul 26, 2019