Detection of false data injection attacks in cyber-physical systems using dynamic invariants

Kiyoshi Nakayama, Nikhil Muralidhar, Chenrui Jin, Ratnesh Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
Pages1023-1030
Number of pages8
ISBN (Electronic)9781728145495
DOIs
StatePublished - Dec 2019
Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
Duration: 16 Dec 201919 Dec 2019

Publication series

NameProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Conference

Conference18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
Country/TerritoryUnited States
CityBoca Raton
Period16/12/1919/12/19

Keywords

  • Anomaly Detection
  • Bad Data Detection
  • Bayesian Filtering
  • Cyber-Physical Systems
  • Dynamic Invariant Network
  • False Data Injection Attacks
  • Kalman Filter
  • Robust Estimation
  • State Estimation

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