Implementation of hybrid artificial intelligence technique to detect covert channels in new generation network protocol IPv6

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Salih, A, Ma, X ORCID: https://orcid.org/0000-0003-0074-4192 and Peytchev, E ORCID: https://orcid.org/0000-0001-5256-4383, 2017. Implementation of hybrid artificial intelligence technique to detect covert channels in new generation network protocol IPv6. In: Benlamri, R and Sparer, M, eds., Leadership, innovation and entrepreneurship as driving forces of the global economy: proceedings of the 2016 International Conference on Leadership, Innovation and Entrepreneurship (ICLIE). Springer proceedings in business and economics . Cham: Springer, pp. 173-190. ISBN 9783319434339

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Abstract

Intrusion detection systems offer monolithic way to detect attacks through monitoring, searching for abnormal characteristics and malicious behavior in network communications. Cyber-attack is performed through using covert channel which currently, is one of the most sophisticated challenges facing network security systems.
Covert channel is used to ex/infiltrate classified information from legitimate targets, consequently, this
manipulation violates network security policy and privacy. The New Generation Internet Protocol version 6 (IPv6) has certain security vulnerabilities and need to be addressed using further advanced techniques. Fuzzy rule is implemented to classify different network attacks as an advanced machine learning technique, meanwhile,
Genetic algorithm is considered as an optimization technique to obtain the ideal fuzzy rule. This paper suggests a novel hybrid covert channel detection system implementing two Artificial Intelligence (AI) techniques; Fuzzy Logic and Genetic Algorithm (FLGA) to gain sufficient and optimal detection rule against covert channel. Our
approach counters sophisticated network unknown attacks through an advanced analysis of deep packet inspection. Results of our suggested system offer high detection rate of 97.7% and a better performance in comparison to previous tested techniques.

Item Type:	Chapter in book
Creators:	Salih, A., Ma, X. and Peytchev, E.
Publisher:	Springer
Place of Publication:	Cham
Date:	2017
ISBN:	9783319434339
Identifiers:	Number Type 10.1007/978-3-319-43434-6_15 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	15 Aug 2016 09:48
Last Modified:	09 Jun 2017 14:05
URI:	https://irep.ntu.ac.uk/id/eprint/28311