Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems
The demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threa...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10445341/ |
| _version_ | 1797277640074199040 |
|---|---|
| author | Mujaheed Abdullahi Hitham Alhussian Norshakirah Aziz Said Jadid Abdulkadir Ayed Alwadain Aminu Aminu Muazu Abubakar Bala |
| author_facet | Mujaheed Abdullahi Hitham Alhussian Norshakirah Aziz Said Jadid Abdulkadir Ayed Alwadain Aminu Aminu Muazu Abubakar Bala |
| author_sort | Mujaheed Abdullahi |
| collection | DOAJ |
| description | The demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threats, artificial intelligence (AI) approaches are employed. However, AI models struggle to keep up with the constantly changing attack landscape. This study investigates the application of extreme gradient boosting (XGBoost) and long-short-term memory (LSTM) AI models for cyberattack detection in a CPS. Accuracy, precision, recall, and the F1-score validate the approach as evaluation metrics. The methods were tested on a gas pipeline industrial control system dataset and other benchmark datasets, such as NetML-2020 and IoT-23, which contain various cyberattacks. The performance of the two methods was found to be better than other models such as support vector machine (SVM) and artificial neural networks (ANN) on several evaluation metrics. Finally, we present recommendations for future research. |
| first_indexed | 2024-03-07T15:52:20Z |
| format | Article |
| id | doaj.art-7222ebed701749e3807088038600f121 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T15:52:20Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7222ebed701749e3807088038600f1212024-03-05T00:00:14ZengIEEEIEEE Access2169-35362024-01-0112319883200410.1109/ACCESS.2024.337043610445341Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical SystemsMujaheed Abdullahi0https://orcid.org/0000-0002-0434-5191Hitham Alhussian1https://orcid.org/0000-0003-3947-269XNorshakirah Aziz2https://orcid.org/0000-0001-5563-0286Said Jadid Abdulkadir3https://orcid.org/0000-0003-0038-3702Ayed Alwadain4Aminu Aminu Muazu5https://orcid.org/0000-0001-6789-0579Abubakar Bala6https://orcid.org/0000-0003-1178-2522Computer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaComputer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaComputer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaComputer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaComputer Science Department, Community College, King Saud University, Riyadh, Saudi ArabiaComputer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Seri Iskandar, MalaysiaInterdisciplinary Research Center for Communication Systems and Sensing (IRC-CSS), King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaThe demand for cyber-physical systems (CPSs) has recently increased in various domains, such as smart grids, intelligent transportation, and critical infrastructure. The massive data networks and communication layers generated make CPSs vulnerable to threats and cyberattacks. To mitigate these threats, artificial intelligence (AI) approaches are employed. However, AI models struggle to keep up with the constantly changing attack landscape. This study investigates the application of extreme gradient boosting (XGBoost) and long-short-term memory (LSTM) AI models for cyberattack detection in a CPS. Accuracy, precision, recall, and the F1-score validate the approach as evaluation metrics. The methods were tested on a gas pipeline industrial control system dataset and other benchmark datasets, such as NetML-2020 and IoT-23, which contain various cyberattacks. The performance of the two methods was found to be better than other models such as support vector machine (SVM) and artificial neural networks (ANN) on several evaluation metrics. Finally, we present recommendations for future research.https://ieeexplore.ieee.org/document/10445341/Artificial intelligenceattack detectioncyberattackscyber-physical systemsdeep learningmachine learning |
| spellingShingle | Mujaheed Abdullahi Hitham Alhussian Norshakirah Aziz Said Jadid Abdulkadir Ayed Alwadain Aminu Aminu Muazu Abubakar Bala Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems IEEE Access Artificial intelligence attack detection cyberattacks cyber-physical systems deep learning machine learning |
| title | Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems |
| title_full | Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems |
| title_fullStr | Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems |
| title_full_unstemmed | Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems |
| title_short | Comparison and Investigation of AI-Based Approaches for Cyberattack Detection in Cyber-Physical Systems |
| title_sort | comparison and investigation of ai based approaches for cyberattack detection in cyber physical systems |
| topic | Artificial intelligence attack detection cyberattacks cyber-physical systems deep learning machine learning |
| url | https://ieeexplore.ieee.org/document/10445341/ |
| work_keys_str_mv | AT mujaheedabdullahi comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems AT hithamalhussian comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems AT norshakirahaziz comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems AT saidjadidabdulkadir comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems AT ayedalwadain comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems AT aminuaminumuazu comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems AT abubakarbala comparisonandinvestigationofaibasedapproachesforcyberattackdetectionincyberphysicalsystems |