Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems

This work focuses on validation of attack pattern mining in the context of Industrial Control System (ICS) security. A comprehensive security assessment of an ICS requires generating a large and variety of attack patterns. For this purpose we have proposed a data driven technique to generate attack patterns for an ICS. The proposed technique has been used to generate over 100,000 attack patterns from data gathered from an operational water treatment plant. In this work we present a detailed case study to validate the attack patterns.

Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations

Methods from machine learning are being applied to design Industrial Control Systems resilient to cyber-attacks. Such methods focus on two major areas: the detection of intrusions at the network-level using the information acquired through network packets, and detection of anomalies at the physical process level using data that represents the physical behavior of the system. This survey focuses on four types of methods from machine learning in use for intrusion and anomaly detection, namely, supervised, semi-supervised, unsupervised, and reinforcement learning. Literature available in the public domain was carefully selected, analyzed, and placed in a 7-dimensional space for ease of comparison. The survey is targeted at researchers, students, and practitioners. Challenges associated in using the methods and research gaps are identified and recommendations are made to fill the gaps.

Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning

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 300,000 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.