Attack rules : an adversarial approach to generate attacks for Industrial Control Systems using machine learning

Tools

Umer, Muhammad Azmi and Ahmed, Chuadhry Mujeeb and Jilani, Muhammad Taha and Mathur, Aditya P.; (2021) Attack rules : an adversarial approach to generate attacks for Industrial Control Systems using machine learning. In: CPSIoTSec 2021 - Proceedings of the 2nd Workshop on CPS and IoT Security and Privacy, co-located with CCS 2021. ACM, KOR, 35–40. ISBN 9781450384872 (https://doi.org/10.1145/3462633.3483976)

[thumbnail of Umer-etal-CPSIoTSec-2021-an-adversarial-approach-to-generate-attacks-for-Industrial-Control-Systems]
Preview
 Text. Filename: Umer_etal_CPSIoTSec_2021_an_adversarial_approach_to_generate_attacks_for_Industrial_Control_Systems.pdf
Accepted Author Manuscript
Download (4MB)| Preview

Abstract

Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive the anomaly detection methods in Industrial Control System (ICS). Given that security assessment of an ICS demands that an exhaustive set of possible attack patterns is studied, in this work, we propose an association rule mining-based attack generation technique. The technique has been implemented using data from a Secure Water Treatment plant. The proposed technique was able to generate more than 110,000 attack patterns constituting a vast majority of new attack vectors which were not seen before. Automatically generated attacks improve our understanding of the potential attacks and enable the design of robust attack detection techniques.

ORCID iDs

Umer, Muhammad Azmi, Ahmed, Chuadhry Mujeeb ORCID logoORCID: https://orcid.org/0000-0003-3644-0465, Jilani, Muhammad Taha and Mathur, Aditya P.;
CORE (COnnecting REpositories)