Anomaly Detection and Data Recovery on Mini Batch Distillation Column based Cyber Physical System

The development of industrial revolution 4.0 in industrial sector opened a cyber gap for outsiders to pose a threat to the system. Industrial control systems initially designed to meet SRA (Safety, Reliability, and Availability) priorities are now beginning to be pressed to consider security aspects related to the magnitude of the impact that can be caused due to external attacks. In making a safe Cyber Physical System (CPS) based automation, risk assessment will be used to determine the level risk of threat. Mini distillation column batch based CPS will be implemented as the approach of CPS in industrial sector. Anomaly detection based data-driven model and data recovery method is proposed to lower the impact of attack on this system

Analysis of production data manipulation attacks in petroleum cyber-physical systems

© 2016 ACM. Petroleum Cyber-Physical System (CPS) marks the beginning of a new chapter of the oil and gas industry. Combining vast computational power with intelligent Computer Aided Design (CAD) algorithms, petroleum CPS is capable of precisely modeling the flow of fluids over the entire petroleum reservoir and leveraging the massive field data remotely collected at the production wells. It provides field operators with valuable insights into the geological structure and remaining reserves of the reservoir for optimizing their operational strategies. Despite such benefits, petroleum CPS is vulnerable to various cyberattacks that jeopardize the integrity of the field data collected at production wells. Given manipulated field data, CAD software would generate an inaccurate reservoir model which misleads the field operators. This work is the first to analyze potential cybersecurity attacks in a petroleum CPS. In this paper, an intelligent cyberattack strategy optimization framework is proposed to optimize the malicious manipulation of field data such that the history matching solver generates the most inaccurate reservoir model. Our method is based on the advanced Model Reference Adaptive Search (MRAS) technique, and it can be used to evaluate the worst case impact due to the field data manipulation attacks. Experimental results on a standard petroleum CPS testcase demonstrate that the proposed method can reduce the production quality, measured by the weighted mismatch sum of the bottom hole pressure (BHP), the gas oil ratio (GOR), and the Water Cut (WCT), by up to 99.1% when comparing to a random attack