METHOD OF DETECTING CYBER ATTACKS ON A CYBER PHYSICAL SYSTEM WHICH INCLUDES AT LEAST ONE COMPUTING DEVICE COUPLED TO AT LEAST ONE SENSOR AND/OR ACTUATOR FOR CONTROLLING A PHYSICAL PROCESS

    公开(公告)号:US20200162482A1

    公开(公告)日:2020-05-21

    申请号:US16090031

    申请日:2017-03-28

    Abstract: A method of detecting cyber attacks on a cyber physical system is disclosed, and the system includes at least one computing device coupled to at least one sensor and/or actuator for controlling a physical process. The method comprises: deriving at least one invariant for the computing device, based on a system design of the system or computer code configured to control the system in relation to the physical process or data collected from the system during testing or operation of the system, the invariant defining a set of conditions that enable determination from the sensor and/or actuator regarding process anomalies of the physical process being controlled; configuring the invariant as corresponding computer code; and executing the invariant as the computer code on the computing device to monitor the physical process via the sensor and/or actuator and detect the process anomalies for detecting the cyber attacks.

    ANOMALY DETECTION SYSTEM AND METHOD FOR AN INDUSTRIAL CONTROL SYSTEM

    公开(公告)号:US20240045410A1

    公开(公告)日:2024-02-08

    申请号:US17761991

    申请日:2022-02-04

    CPC classification number: G05B23/0221 G05B23/0254 G05B23/0248

    Abstract: An anomaly detection method includes determining state variables of an industrial control system based on a system design of the industrial control system; determining invariants governing the state variables based on the system design; receiving historical measurement data of the state variables of each invariant from the industrial control system; constructing a set of behavioural models for each invariant using a set of machine learning algorithms and the historical measurement data of the respective state variables, the behavioural models representing normal behaviour of the respective state variables; predicting measurement data of the state variables of each invariant using the behavioural models and the historical measurement data of the respective state variables; receiving current measurement data of the state variables during operation of the industrial control system; and detecting the anomalies based on deviations between the current measurement data and predicted measurement data of the state variables of each invariant.

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