Cheng Feng (Imperial College London & Siemens Corporate Technology), Venkata Reddy Palleti (Singapore University of Technology and Design), Aditya Mathur (Singapore University of Technology and Design), Deeph Chana (Imperial College London)

Industrial Control Systems (ICS) consisting of integrated hardware and software components designed to monitor and control a variety of industrial processes, are typically deployed in critical infrastructures such as water treatment plants, power grids and gas pipelines. Unlike conventional IT systems, the consequences of deviations from normal operation in ICS have the potential to cause significant physical damage to equipment, the environment and even human life. The active monitoring of invariant rules that define the physical conditions that must be maintained for the normal operation of ICS provides a means to improve the security and dependability of such systems by which early detection of anomalous system states may be achieved, allowing for timely mitigating actions -- such as fault checking, system shutdown -- to be taken. Generally, invariant rules are pre-defined by system engineers during the design phase of a given ICS build. However, this manually intensive process is costly, error-prone and, in typically complex systems, sub-optimal. In this paper we propose a novel framework that is designed to systematically generate invariant rules from information contained within ICS operational data logs, using a combination of several machine learning and data mining techniques. The effectiveness of our approach is demonstrated by experiments on two real world ICS testbeds: a water distribution system and a water treatment plant. We show that sets of invariant rules, far larger than those defined manually, can be successfully derived by our framework and that they may be used to deliver significant improvements in anomaly detection compared with the invariant rules defined by system engineers as well as the commonly used residual error-based anomaly detection model for ICS.

View More Papers

Balancing Image Privacy and Usability with Thumbnail-Preserving Encryption

Kimia Tajik (Oregon State University), Akshith Gunasekaran (Oregon State University), Rhea Dutta (Cornell University), Brandon Ellis (Oregon State University), Rakesh B. Bobba (Oregon State University), Mike Rosulek (Oregon State University), Charles V. Wright (Portland State University), Wu-Chi Feng (Portland State University)

Read More

Anonymous Multi-Hop Locks for Blockchain Scalability and Interoperability

Giulio Malavolta (Friedrich-Alexander University Erlangen-Nürnberg), Pedro Moreno Sanchez (TU Wien), Clara Schneidewind (TU Wien), Aniket Kate (Purdue University), Matteo Maffei (TU Wien)

Read More

Neuro-Symbolic Execution: Augmenting Symbolic Execution with Neural Constraints

Shiqi Shen (National University of Singapore), Shweta Shinde (National University of Singapore), Soundarya Ramesh (National University of Singapore), Abhik Roychoudhury (National University of Singapore), Prateek Saxena (National University of Singapore)

Read More

On the Challenges of Geographical Avoidance for Tor

Katharina Kohls (Ruhr-University Bochum), Kai Jansen (Ruhr-University Bochum), David Rupprecht (Ruhr-University Bochum), Thorsten Holz (Ruhr-University Bochum), Christina Pöpper (New York University Abu Dhabi)

Read More