Process skew: fingerprinting the process for anomaly detection in industrial control systems

Chuadhry Mujeeb Ahmed, Jay Prakash, Rizwan Qadeer, Anand Agrawal, Jianying Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

3 Citations (Scopus)

Abstract

In an Industrial Control System (ICS), its complex network of sensors, actuators and controllers have raised security concerns. In this paper, we proposed a technique called Process Skew that uses the small deviations in the ICS process (herein called as a process fingerprint) for anomaly detection. The process fingerprint appears as noise in sensor measurements due to the process fluctuations. Such a fingerprint is unique to a process due to the intrinsic operational constraints of the physical process. We validated the proposed scheme using the data from a real-world water treatment testbed. Our results show that we can effectively identify a process based on its fingerprint, and detect process anomaly with a very low false-positive rate.
Original languageEnglish
Title of host publicationWiSec'20
Subtitle of host publicationProceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Place of PublicationNew York
Pages219-230
Number of pages12
DOIs
Publication statusPublished - 8 Jul 2020
Event13th ACM Conference on Security and Privacy in Wireless and Mobile Networks -
Duration: 6 Jul 20209 Jul 2020
Conference number: 13

Conference

Conference13th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Abbreviated titleACM WiSec 2020
Period6/07/209/07/20

Keywords

  • CPS security
  • critical infrastructure
  • cyber physical systems
  • sensor attacks
  • sensor security

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