Research on a Identifying Maliciously Manipulated Measurement Data Based on Characteristics of Cyber-Physical System

Intelligent attackers targeting critical infrastructure frequently conceal their attack strategies until their objectives are accomplished. Despite extensive research on detecting manipulated data, much of this research fails to consider the real-world noise inherent in cyber-physical systems. This paper addresses these shortcomings by integratively applying an Unscented Kalman Filter and an Entropy approach. The efficacy of this method is confirmed through its application to a publicly available industrial control system security dataset, demonstrating its capability to accurately detect manipulated data.