Activities per year
Abstract
Machine Learning (ML) has proven to be effective
in many application domains. However, ML methods can be
vulnerable to adversarial attacks, in which an attacker tries to fool
the classification/prediction mechanism by crafting the input data.
In the case of ML-based Network Intrusion Detection Systems
(NIDSs), the attacker might use their knowledge of the intrusion
detection logic to generate malicious traffic that remains undetected. One way to solve this issue is to adopt adversarial training,
in which the training set is augmented with adversarial traffic
samples. This paper presents an adversarial training approach
called GADoT, which leverages a Generative Adversarial Network
(GAN) to generate adversarial DDoS samples for training. We
show that a state-of-the-art NIDS with high accuracy on popular
datasets can experience more than 60% undetected malicious
flows under adversarial attacks. We then demonstrate how this
score drops to 1.8% or less after adversarial training using
GADoT.
Original language | English |
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Title of host publication | Ninth IEEE Conference on Communications and Network Security (IEEE CNS 2021): Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
DOIs | |
Publication status | Published - 10 Feb 2022 |
Event | IEEE Conference on Communications and Network Security - Virtual Duration: 04 Oct 2021 → 06 Oct 2021 https://cns2021.ieee-cns.org/ |
Conference
Conference | IEEE Conference on Communications and Network Security |
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Period | 04/10/2021 → 06/10/2021 |
Internet address |
Fingerprint
Dive into the research topics of 'GaDoT: GAN-based Adversarial Training for Robust DDoS Attack Detection'. Together they form a unique fingerprint.Activities
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AI: Adversarial Attacks
Scott-Hayward, S. (Invited speaker)
08 Feb 2023Activity: Talk or presentation types › Invited talk
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99.99% accurate - What's the problem?
Scott-Hayward, S. (Keynote speaker)
02 Sept 2022Activity: Talk or presentation types › Invited or keynote talk at national or international conference
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Cyber AI - Panel
Scott-Hayward, S. (Speaker)
17 May 2022Activity: Talk or presentation types › Oral presentation
Research output
- 21 Citations
- 1 Article
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Securing AI-based Security Systems
Scott-Hayward, S., 01 Jun 2022, GCSP Strategic Security Analysis, 25.Research output: Contribution to specialist publication › Article
Open AccessFile