Introducing randomness into univariate control chart sampling strategies for enhancing security against cyber‐physical attacks in production systems

Abstract

With recent technological advancements, production systems have become more susceptible to cyber‐physical attacks. Such attacks can be intelligently designed to both alter product dimensions and avoid detection by current Quality Control (QC) tools. The objective of this work is to assess the performance of random sampling strategies for control charting in detecting cyber‐physical attacks. More specifically, the methodology adopts traditional random sampling approaches used for inspection and applies them to univariate control charts. Different random sampling strategies are discussed and their performances under varying attack scenarios are evaluated. In addition, new control chart performance metrics were developed for a more in‐depth analysis, to guide practitioners in assessing how well these control charts increase their robustness to cyber‐physical attacks.

A novel probabilistic modeling for multilateral random attacks in cyber‐physical system reliability analysis

“…Additionally, literature 25 explores the reliability of most voting systems when subjected to cyber threats, considering three multiple attack scenarios, including targeted, random, and dynamic attacks. In literature, 26 a traditional random sampling approach is applied for cyber‐attack detection, integrating it into univariate control charts. This literature also discusses various random sampling strategies and evaluates their performance in different attack scenarios.…”