A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems
<p dir="ltr">In light of the advancement of the technologies used in industrial control systems, securing their operation has become crucial, primarily since their activity is consistently associated with integral elements related to the environment, the safety and health of people,...
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2023
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| _version_ | 1864513527442571264 |
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| author | Mariam Elnour (14147790) |
| author2 | Mohammad Noorizadeh (16891371) Mohammad Shakerpour (17983747) Nader Meskin (14147796) Khaled Khan (17280781) Raj Jain (17280784) |
| author2_role | author author author author author |
| author_facet | Mariam Elnour (14147790) Mohammad Noorizadeh (16891371) Mohammad Shakerpour (17983747) Nader Meskin (14147796) Khaled Khan (17280781) Raj Jain (17280784) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mariam Elnour (14147790) Mohammad Noorizadeh (16891371) Mohammad Shakerpour (17983747) Nader Meskin (14147796) Khaled Khan (17280781) Raj Jain (17280784) |
| dc.date.none.fl_str_mv | 2023-08-07T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3303015 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Machine_Learning_Based_Framework_for_Real-Time_Detection_and_Mitigation_of_Sensor_False_Data_Injection_Cyber-Physical_Attacks_in_Industrial_Control_Systems/25239478 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Hidden Markov models Power systems Artificial neural networks Analytical models Mathematical models Machine learning Predictive models Attack detection attack mitigation industrial control system (ICS) false data injection (FDI) support vector machine (SVM) |
| dc.title.none.fl_str_mv | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In light of the advancement of the technologies used in industrial control systems, securing their operation has become crucial, primarily since their activity is consistently associated with integral elements related to the environment, the safety and health of people, the economy, and many others. This work presents a distributed, machine learning based attack detection and mitigation framework for sensor false data injection cyber-physical attacks in industrial control systems. It is developed using the system’s standard operational data and validated using a hybrid testbed of a reverse osmosis plant. A MATLAB/Simulink-based simulation model of the process validated with actual data from a local plant is used. The control system is implemented using Siemens S7-1200 programmable logic controllers with 200SP Distributed Input/Output modules. The proposed solution can be adopted in the existing industrial control systems and demonstrated effective performance in real-time detection and mitigation of actual cyber-physical attacks launched by compromising the communication links between the process and the programmable logic controllers.<br></p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3303015" target="_blank">https://dx.doi.org/10.1109/access.2023.3303015</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8d9918fe104969d8470c099358b69af8 |
| identifier_str_mv | 10.1109/access.2023.3303015 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239478 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control SystemsMariam Elnour (14147790)Mohammad Noorizadeh (16891371)Mohammad Shakerpour (17983747)Nader Meskin (14147796)Khaled Khan (17280781)Raj Jain (17280784)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringHidden Markov modelsPower systemsArtificial neural networksAnalytical modelsMathematical modelsMachine learningPredictive modelsAttack detectionattack mitigationindustrial control system (ICS)false data injection (FDI)support vector machine (SVM)<p dir="ltr">In light of the advancement of the technologies used in industrial control systems, securing their operation has become crucial, primarily since their activity is consistently associated with integral elements related to the environment, the safety and health of people, the economy, and many others. This work presents a distributed, machine learning based attack detection and mitigation framework for sensor false data injection cyber-physical attacks in industrial control systems. It is developed using the system’s standard operational data and validated using a hybrid testbed of a reverse osmosis plant. A MATLAB/Simulink-based simulation model of the process validated with actual data from a local plant is used. The control system is implemented using Siemens S7-1200 programmable logic controllers with 200SP Distributed Input/Output modules. The proposed solution can be adopted in the existing industrial control systems and demonstrated effective performance in real-time detection and mitigation of actual cyber-physical attacks launched by compromising the communication links between the process and the programmable logic controllers.<br></p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3303015" target="_blank">https://dx.doi.org/10.1109/access.2023.3303015</a></p>2023-08-07T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3303015https://figshare.com/articles/journal_contribution/A_Machine_Learning_Based_Framework_for_Real-Time_Detection_and_Mitigation_of_Sensor_False_Data_Injection_Cyber-Physical_Attacks_in_Industrial_Control_Systems/25239478CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252394782023-08-07T06:00:00Z |
| spellingShingle | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems Mariam Elnour (14147790) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Hidden Markov models Power systems Artificial neural networks Analytical models Mathematical models Machine learning Predictive models Attack detection attack mitigation industrial control system (ICS) false data injection (FDI) support vector machine (SVM) |
| status_str | publishedVersion |
| title | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| title_full | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| title_fullStr | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| title_full_unstemmed | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| title_short | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| title_sort | A Machine Learning Based Framework for Real-Time Detection and Mitigation of Sensor False Data Injection Cyber-Physical Attacks in Industrial Control Systems |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Hidden Markov models Power systems Artificial neural networks Analytical models Mathematical models Machine learning Predictive models Attack detection attack mitigation industrial control system (ICS) false data injection (FDI) support vector machine (SVM) |