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Locally Weighted Classifiers for Detection of Neighbour Discovery Protocol DDoS and Replayed Attacks

Alsadhan, A, Hussain, A, Liatsis, P, Alani, M, Tawfik, H, Kendrick, P and Francis, H (2019) Locally Weighted Classifiers for Detection of Neighbour Discovery Protocol DDoS and Replayed Attacks. Transactions on Emerging Telecommunications Technologies. ISSN 2161-3915

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The Internet of Thing (IoT) requires more IP addresses than Internet Protocol version 4 can offer. To solve this problem, Internet Protocol version 6 was developed to expand the availability of address spaces. Moreover, it supports hierarchical address allocation methods, which can facilitate route aggregation, thus limiting expansion of routing tables. An important feature of the Internet Protocol version 6 (IPv6) suites is the Neighbour Discovery Protocol (NDP), which is geared towards substitution of the Address Resolution Protocol in router discovery, and function redirection in Internet Protocol version 4. However, NDP is vulnerable to Denial of Service (DoS) attacks. In this contribution, we present a novel detection method for Distributed Denial of Service (DDoS) attacks, launched using NDP in IPv6. The proposed system uses flow-based network representation, instead of packet-based. It exploits the advantages of Locally Weighted Learning techniques, with three different machine learning models as its base learners. Simulation studies demonstrate that the intrusion detection method does not suffer from overfitting issues, offers lower computation costs and complexity, while exhibiting high accuracy rates. In summary, the proposed system uses 6 features, extracted from our bespoke dataset and is capable of detecting DDoS attacks with 99% accuracy and replayed attacks with an accuracy of 91.17%, offering a marked improvement in detection performance over state-of-the-art approaches.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Alsadhan, A, Hussain, A, Liatsis, P, et al. Locally weighted classifiers for detection of neighbor discovery protocol distributed denial‐of‐service and replayed attacks. Trans Emerging Tel Tech. 2019;e3700. , which has been published in final form at https://doi.org/10.1002/ett.3700 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Wiley
Date Deposited: 21 Jun 2019 10:28
Last Modified: 04 Sep 2021 09:15
DOI or ID number: 10.1002/ett.3700
URI: https://researchonline.ljmu.ac.uk/id/eprint/10918
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