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Vulnerability in industrial control systems (ICS) has increased radically in the past few decades. This can be accounted to reasons including accessibility of ICS through Internet, development of sophisticated attack methods, and advancement in Internet of Things (IoT). The damage that can be caused by attackers on insecure ICS could cost hundreds of human lives and a huge state economy. Therefore, having such systems every where around us makes the concern for security nothing but a priority. Programmable logic controllers (PLCs) are the central devices of ICS and also a target to attacks which aim at gaining access and privilege to the control logic of the controller. A successful alteration or intrusion of the control logic by attackers can have a catastrophic effect on the plant. In this paper, control logic intrusion detection methodology for PLC-based control systems is proposed. The methodology implements the detection process by comparing a potentially intruded PLC program with a trusted version of the program. In order to achieve this goal, a scheme is proposed that operates in sequence of stages by first translating the PLC program to formal models (based on previous research work), then translating the formal models to graphs followed by performing a comparison between a trusted system model graph and a potentially intruded system graph. In the last stage of the methodology, graph discrepancy analysis is made in order to identify any intrusions. For demonstration purposes, a water level control system is presented as a case study, which is modeled using UPPAAL toolbox. We have verified our methodology by developing an in-house software. Our test results prove the concept that intrusions can be shown as discrepancies in the graphs generated from the UPPAAL-based formal modeling, which can be detected utilizing the proposed graph comparison approach. The premise of our study is that logic intrusions in PLC based ICS can be identified by the changes in the PLC code, and the methodology we proposed can successfully detect those changes by observing the code's graph model.

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