Zhou, Q. and Pezaros, D. P. (2019) BIDS: Bio-Inspired, Collaborative Intrusion Detection for Software Defined Networks. In: 53rd IEEE International Conference on Communications (IEEE ICC 2019), Shanghai, China, 20-24 May 2019, ISBN 9781538680889 (doi: 10.1109/ICC.2019.8761410)
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179728.pdf - Accepted Version 547kB |
Abstract
With network attacks becoming more sophisticated and unpredictable, detecting their onset and mitigating their effects in an automated manner become increasingly challenging. Lightweight and agile detection mechanisms that are able to detect zero-day attacks are in great need. High true-negative rate and low false-positive rate are the most important indicators for a intrusion detection system. In this paper, we exploit the logically-centralised view of Software-Defined Networking (SDN) to increase true-negative rate and lower false-positive rate in a intrusion detection system based on the Artificial Immune System (AIS). We propose the use of an antibody fuser in the controller to merge and fuse the mature antibody sets trained in the individual switches and turn the real intrusion records each switch has seen into antibodies. Our results show that both the false-positive rate and true-negative rate experience significant improvement with the number of local antibody sets fused grows, consuming less cpu usage overhead. A peak improvement can reach over 80% when antibody sets from all switches are taken into consideration.
| Item Type: | Conference Proceedings |
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| Additional Information: | This research has been supported in part by the UK Engineering and Physical Sciences Research Council (EPSRC) projects EP/N033957/1, and EP/P004024/1; by the European Cooperation in Science and Technology (COST) Action CA 15127: RECODIS – Resilient communication and services; by the EU H2020 GNFUV Project RAWFIE-OC2-EXPSCI (Grant No. 645220), under the EC FIRE+ initiative; and by the Huawei Innovation Research Program (Grant No. 300952). |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Zhou, Miss Qianru and Pezaros, Professor Dimitrios |
| Authors: | Zhou, Q., and Pezaros, D. P. |
| College/School: | College of Science and Engineering > School of Computing Science |
| ISSN: | 1938-1883 |
| ISBN: | 9781538680889 |
| Copyright Holders: | Copyright © 2019 IEEE |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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