Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review
In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/2/198 |
| _version_ | 1797494512272015360 |
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| author | Mujaheed Abdullahi Yahia Baashar Hitham Alhussian Ayed Alwadain Norshakirah Aziz Luiz Fernando Capretz Said Jadid Abdulkadir |
| author_facet | Mujaheed Abdullahi Yahia Baashar Hitham Alhussian Ayed Alwadain Norshakirah Aziz Luiz Fernando Capretz Said Jadid Abdulkadir |
| author_sort | Mujaheed Abdullahi |
| collection | DOAJ |
| description | In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations. |
| first_indexed | 2024-03-10T01:35:22Z |
| format | Article |
| id | doaj.art-42fe76c7f5cd4033bdd90b6a164a46df |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T01:35:22Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-42fe76c7f5cd4033bdd90b6a164a46df2023-11-23T13:33:51ZengMDPI AGElectronics2079-92922022-01-0111219810.3390/electronics11020198Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature ReviewMujaheed Abdullahi0Yahia Baashar1Hitham Alhussian2Ayed Alwadain3Norshakirah Aziz4Luiz Fernando Capretz5Said Jadid Abdulkadir6Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaInstitute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), Kajang 43000, MalaysiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaComputer Science Department, Community College, King Saud University, Riyadh 145111, Saudi ArabiaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaDepartment of Electrical & Computer Engineering, Western University, London, ON N6A5B9, CanadaDepartment of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, MalaysiaIn recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.https://www.mdpi.com/2079-9292/11/2/198systematic literature reviewinternet of thingsartificial intelligencemachine learningdeep learningintrusion detection systems |
| spellingShingle | Mujaheed Abdullahi Yahia Baashar Hitham Alhussian Ayed Alwadain Norshakirah Aziz Luiz Fernando Capretz Said Jadid Abdulkadir Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review Electronics systematic literature review internet of things artificial intelligence machine learning deep learning intrusion detection systems |
| title | Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review |
| title_full | Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review |
| title_fullStr | Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review |
| title_full_unstemmed | Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review |
| title_short | Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review |
| title_sort | detecting cybersecurity attacks in internet of things using artificial intelligence methods a systematic literature review |
| topic | systematic literature review internet of things artificial intelligence machine learning deep learning intrusion detection systems |
| url | https://www.mdpi.com/2079-9292/11/2/198 |
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