Cyber-Physical System, Security, Machine Learning

CPS Security

We offer intelligent techniques for building scalable intrusion detection system for recourse-constrained Cyber-Physical Systems (CPSes).


ARTINALI# is a Bayesian-based search and score technique that identifies the critical points at which to instrument a CPS. Given a set of security monitors that observe run-time behavior of the system, a set of specifications that verify the correct behavior of the system, and statistics gathered from fault injection, ARTINALI# discovers a small set of locations and a rich set of specifications that yield full attack coverage with low (memory and time) overhead. We deploy ARTINALI# to construct an intrusion detection system for two classes CPSes, and demonstrate that our technique reduces the number of security monitors by 64% on average, leading to 52% and 69% reductions in memory and runtime overhead respectively.

Additional Information
  • People: Maryam R.Aliabadi
  • ARTINALI Paper: Dynamic Invariant Detection for Cyber-Physical System Security , FSE 2017
  • ARTINALI# Paper: An Efficient Intrusion Detection Technique for Resource-Constrained Cyber-Physical Systems , IJCIP 2021
  • ARTINALI++ Paper: Multi-dimensional Specification Mining for Complex Cyber-Physical System Security , JSS 2021
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    Systopia lab is supported by a number of government and industrial sources, including Cisco Systems, the Communications Security Establishment Canada, Intel Research, the National Sciences and Engineering Research Council of Canada (NSERC), Network Appliance, Office of the Privacy Commissioner of Canada, and the National Science Foundation (NSF).