Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools
The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other tech...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/15/7/244 |
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| author | Obinna Johnphill Ali Safaa Sadiq Feras Al-Obeidat Haider Al-Khateeb Mohammed Adam Taheir Omprakash Kaiwartya Mohammed Ali |
| author_facet | Obinna Johnphill Ali Safaa Sadiq Feras Al-Obeidat Haider Al-Khateeb Mohammed Adam Taheir Omprakash Kaiwartya Mohammed Ali |
| author_sort | Obinna Johnphill |
| collection | DOAJ |
| description | The rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems. |
| first_indexed | 2024-03-11T01:03:27Z |
| format | Article |
| id | doaj.art-2b64e353cf374d7f804575dfc48ccbc8 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-11T01:03:27Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-2b64e353cf374d7f804575dfc48ccbc82023-11-18T19:26:59ZengMDPI AGFuture Internet1999-59032023-07-0115724410.3390/fi15070244Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and ToolsObinna Johnphill0Ali Safaa Sadiq1Feras Al-Obeidat2Haider Al-Khateeb3Mohammed Adam Taheir4Omprakash Kaiwartya5Mohammed Ali6Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UKDepartment of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UKCollege of Technological Innovation, Zayed University, Abu Dhabi P.O. Box 144534, United Arab EmiratesCyber Security Innovation (CSI) Research Centre, Aston Business School, Aston St, Birmingham B4 7ET, UKFaculty of Technology Sciences, Zalingei University, Zalingei P.O. Box 6, Central Darfur, SudanDepartment of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UKDepartment of Computer Science, King Khalid University, Abha 61421, Saudi ArabiaThe rapid advancement of networking, computing, sensing, and control systems has introduced a wide range of cyber threats, including those from new devices deployed during the development of scenarios. With recent advancements in automobiles, medical devices, smart industrial systems, and other technologies, system failures resulting from external attacks or internal process malfunctions are increasingly common. Restoring the system’s stable state requires autonomous intervention through the self-healing process to maintain service quality. This paper, therefore, aims to analyse state of the art and identify where self-healing using machine learning can be applied to cyber–physical systems to enhance security and prevent failures within the system. The paper describes three key components of self-healing functionality in computer systems: anomaly detection, fault alert, and fault auto-remediation. The significance of these components is that self-healing functionality cannot be practical without considering all three. Understanding the self-healing theories that form the guiding principles for implementing these functionalities with real-life implications is crucial. There are strong indications that self-healing functionality in the cyber–physical system is an emerging area of research that holds great promise for the future of computing technology. It has the potential to provide seamless self-organising and self-restoration functionality to cyber–physical systems, leading to increased security of systems and improved user experience. For instance, a functional self-healing system implemented on a power grid will react autonomously when a threat or fault occurs, without requiring human intervention to restore power to communities and preserve critical services after power outages or defects. This paper presents the existing vulnerabilities, threats, and challenges and critically analyses the current self-healing theories and methods that use machine learning for cyber–physical systems.https://www.mdpi.com/1999-5903/15/7/244cyber–physical systemcybersecuritythreat toleranceself-healingintrusion detectionmachine-learning algorithms |
| spellingShingle | Obinna Johnphill Ali Safaa Sadiq Feras Al-Obeidat Haider Al-Khateeb Mohammed Adam Taheir Omprakash Kaiwartya Mohammed Ali Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools Future Internet cyber–physical system cybersecurity threat tolerance self-healing intrusion detection machine-learning algorithms |
| title | Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools |
| title_full | Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools |
| title_fullStr | Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools |
| title_full_unstemmed | Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools |
| title_short | Self-Healing in Cyber–Physical Systems Using Machine Learning: A Critical Analysis of Theories and Tools |
| title_sort | self healing in cyber physical systems using machine learning a critical analysis of theories and tools |
| topic | cyber–physical system cybersecurity threat tolerance self-healing intrusion detection machine-learning algorithms |
| url | https://www.mdpi.com/1999-5903/15/7/244 |
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