AI-based techniques for DDoS Attack Detection in WSN: a systematic literature review
Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerab...
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| Format: | Article |
| Language: | English English |
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Science Publications
2020
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| Online Access: | http://irep.iium.edu.my/81267/ http://irep.iium.edu.my/81267/1/Ashik.pdf http://irep.iium.edu.my/81267/7/81267_AI-based%20techniques%20for%20DDoS%20Attack%20Detection%20in%20WSN_SCOPUS.pdf |
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| author | Mohammed, Al-Naeem Mohammed, Ashikur Rahman Abubakar, Adamu Rahman, M.M. Hafizur |
| author_facet | Mohammed, Al-Naeem Mohammed, Ashikur Rahman Abubakar, Adamu Rahman, M.M. Hafizur |
| author_sort | Mohammed, Al-Naeem |
| building | IIUM Repository |
| collection | Online Access |
| description | Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerable attacks that can be defined as attacks launched from multiple ends into a set of legitimate sensor nodes in the WSN to drain their inadequate energy resources. Nowadays, Artificial intelligence techniques are performing better accuracy than the traditional methods to detect intrusion for the various attack. This Systematic Literature Review (SLR) attempts to investigate the current status of DDoS detection techniques and to identify the most capable and effective detection system using artificial intelligence to detect distributed DoS attack. Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) statement is used to conduct this review. Based on 15 out of 983 that met inclusion criteria, Support Vector Machine (SVM) and Artificial Neural Network (ANN) is the most used AI-based techniques to detect distributed denial of service attack in the wireless sensor network. The performance of AI techniques-based detection system for DDoS attack in WSN is remarkable. |
| first_indexed | 2025-11-14T17:51:24Z |
| format | Article |
| id | iium-81267 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:51:24Z |
| publishDate | 2020 |
| publisher | Science Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-812672020-12-10T08:11:50Z http://irep.iium.edu.my/81267/ AI-based techniques for DDoS Attack Detection in WSN: a systematic literature review Mohammed, Al-Naeem Mohammed, Ashikur Rahman Abubakar, Adamu Rahman, M.M. Hafizur QA76 Computer software Wireless Sensor Networks (WSNs) are currently being used in various industries such as healthcare, engineering, the environment and so on. Security is a significant issue for WSN due to its infrastructure and limited physical security. Distributed Denial of Service (DDoS) is one of the most vulnerable attacks that can be defined as attacks launched from multiple ends into a set of legitimate sensor nodes in the WSN to drain their inadequate energy resources. Nowadays, Artificial intelligence techniques are performing better accuracy than the traditional methods to detect intrusion for the various attack. This Systematic Literature Review (SLR) attempts to investigate the current status of DDoS detection techniques and to identify the most capable and effective detection system using artificial intelligence to detect distributed DoS attack. Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) statement is used to conduct this review. Based on 15 out of 983 that met inclusion criteria, Support Vector Machine (SVM) and Artificial Neural Network (ANN) is the most used AI-based techniques to detect distributed denial of service attack in the wireless sensor network. The performance of AI techniques-based detection system for DDoS attack in WSN is remarkable. Science Publications 2020-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/81267/1/Ashik.pdf application/pdf en http://irep.iium.edu.my/81267/7/81267_AI-based%20techniques%20for%20DDoS%20Attack%20Detection%20in%20WSN_SCOPUS.pdf Mohammed, Al-Naeem and Mohammed, Ashikur Rahman and Abubakar, Adamu and Rahman, M.M. Hafizur (2020) AI-based techniques for DDoS Attack Detection in WSN: a systematic literature review. Journal of Computer Science, 16 (6). pp. 848-855. ISSN 1549-3636 E-ISSN 1552-6607 https://thescipub.com/abstract/10.3844/jcssp.2020.848.855 10.3844/jcssp.2020.848.855 |
| spellingShingle | QA76 Computer software Mohammed, Al-Naeem Mohammed, Ashikur Rahman Abubakar, Adamu Rahman, M.M. Hafizur AI-based techniques for DDoS Attack Detection in WSN: a systematic literature review |
| title | AI-based techniques for DDoS Attack Detection in WSN: a
systematic literature review |
| title_full | AI-based techniques for DDoS Attack Detection in WSN: a
systematic literature review |
| title_fullStr | AI-based techniques for DDoS Attack Detection in WSN: a
systematic literature review |
| title_full_unstemmed | AI-based techniques for DDoS Attack Detection in WSN: a
systematic literature review |
| title_short | AI-based techniques for DDoS Attack Detection in WSN: a
systematic literature review |
| title_sort | ai-based techniques for ddos attack detection in wsn: a
systematic literature review |
| topic | QA76 Computer software |
| url | http://irep.iium.edu.my/81267/ http://irep.iium.edu.my/81267/ http://irep.iium.edu.my/81267/ http://irep.iium.edu.my/81267/1/Ashik.pdf http://irep.iium.edu.my/81267/7/81267_AI-based%20techniques%20for%20DDoS%20Attack%20Detection%20in%20WSN_SCOPUS.pdf |