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Machine-learning based network intrusion detection systems (ML-NIDS) are increasingly popular in the fight against network attacks. In particular, promising detection results have been demonstrated in conjunction with Software-Defined Networks (SDN), in which the logically centralized control plane provides access to data from across the network. However,research into adversarial attacks against machine learning classifiers has highlighted vulnerabilities in a number of fields.These vulnerabilities raise concerns about the implementation of similar classifiers in anomaly-based NIDSs within SDNs. Inthis work, we investigate the viability of adversarial attacks against classifiers in this field. We implement an anomaly-based NIDS, Neptune, as a target platform that utilises a number of different machine learning classifiers and traffic flow features. We develop an adversarial test tool, Hydra, to evaluate the impact of adversarial evasion classifier attacks against Neptune with the goal of lowering the detection rate of malicious network traffic. The results demonstrate that with the perturbation ofa few features, the detection accuracy of a specific SYN flood Distributed Denial of Service (DDoS) attack by Neptune decreases from 100% to 0% across a number of classifiers. Based on these results, recommendations are made as to how to increase the robustness of classifiers against the demonstrated attacks.
|Title of host publication||IEEE Conference on Network Functions Virtualization and Software Defined Networks 12/11/2019 → 14/11/2019 Dallas, United States|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 19 Mar 2020|
|Event||IEEE Conference on Network Functions Virtualization and Software Defined Networks - Dallas, United States|
Duration: 12 Nov 2019 → 14 Nov 2019
|Conference||IEEE Conference on Network Functions Virtualization and Software Defined Networks|
|Abbreviated title||IEEE NFV-SDN|
|Period||12/11/2019 → 14/11/2019|
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Sandra Scott-Hayward (Keynote speaker)02 Sep 2022
Activity: Talk or presentation types › Invited or keynote talk at national or international conference
Cyber AI - Panel
Sandra Scott-Hayward (Speaker)17 May 2022
Activity: Talk or presentation types › Oral presentation
Best Conference Paper Award, IEEE NFV-SDN 2019
Aiken, James (Recipient) & Scott-Hayward, Sandra (Recipient), 2019
Prize: Prize (including medals and awards)
- 7 Citations
- 1 Article
Securing AI-based Security SystemsScott-Hayward, S., 01 Jun 2022, GCSP Strategic Security Analysis, 25.
Research output: Contribution to specialist publication › ArticleOpen AccessFile