Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems

• URL: http://arxiv.org/abs/2004.05005v1
• Date: Fri, 10 Apr 2020 12:05:33 GMT
• Title: Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems
• Authors: Eirini Anthi, Lowri Williams, Matilda Rhode, Pete Burnap, Adam Wedgbury
• Abstract summary: This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples. It also explores how such samples can support the robustness of supervised models using adversarial training. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present.
• Score: 2.86989372262348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.

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