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

Muhammad Azmi Umer, Muhammad Taha Jilani, Chuadhry Mujeeb Ahmed, Aditya P. Mathur

Research output: Contribution to conferencePaperpeer-review

Abstract

Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive theanomaly 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, wepropose an association rule mining-based attack generation technique. The technique has been implemented using data from aSecure Water Treatment plant. The proposed technique was ableto generate more than 300,000 attack patterns constituting a vastmajority of new attack vectors which were not seen before. Automatically generated attacks improve our understanding of thepotential attacks and enable the design of robust attack detectiontechniques.
Original languageEnglish
Number of pages6
Publication statusAccepted/In press - 22 Aug 2021
EventCPSIoTSec 2021: The 2nd Joint Workshop on CPS & IoT Security and Privacy - Seoul, Korea, Republic of
Duration: 15 Nov 202115 Nov 2021
https://cpsiotsec.github.io/

Conference

ConferenceCPSIoTSec 2021
Country/TerritoryKorea, Republic of
CitySeoul
Period15/11/2115/11/21
Internet address

Keywords

  • attack detection
  • attack generation
  • ICS security
  • machine learning
  • adversarial learning
  • association rule mining

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