Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review
The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend cri...
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| Language: | en |
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Mesopotamian Academic Press
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41709/1/Distributed%20denial%20of%20service%20attack%20Detection%20in%20IoT%20networks.pdf http://umpir.ump.edu.my/id/eprint/41709/ https://doi.org/10.58496/MJCS/2024/004 |
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| author | Nuhu Ahmad, Abdul hafiz Anis Farihan, Mat Raffei Mohd Faizal, Ab Razak Ahmad Syafadhli, Abu Bakar |
| author_facet | Nuhu Ahmad, Abdul hafiz Anis Farihan, Mat Raffei Mohd Faizal, Ab Razak Ahmad Syafadhli, Abu Bakar |
| author_sort | Nuhu Ahmad, Abdul hafiz |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks. |
| format | Article |
| id | my.ump.umpir.41709 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2024 |
| publisher | Mesopotamian Academic Press |
| record_format | eprints |
| spelling | my.ump.umpir.417092024-07-31T03:28:58Z http://umpir.ump.edu.my/id/eprint/41709/ Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review Nuhu Ahmad, Abdul hafiz Anis Farihan, Mat Raffei Mohd Faizal, Ab Razak Ahmad Syafadhli, Abu Bakar QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) The explosive growth of Internet of Things (IoT) devices has led to escalating threats from distributed denial of service (DDoS) attacks. Moreover, the scale and heterogeneity of IoT environments pose unique security challenges, and intelligent solutions tailored for the IoT are needed to defend critical infrastructure. The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. Additionally, feature fusion approaches have gained traction in enhancing the performance of deep learning models by combining complementary feature sets extracted from multiple data sources. This paper aims to provide a comprehensive literature review focused specifically on deep learning techniques and feature fusion for DDoS attack detection in IoT networks. Studies employing diverse deep learning models and feature fusion techniques are analysed, highlighting key trends and developments in this crucial domain. This review provides several significant contributions, including an overview of various types of DDoS attacks, a comparison of existing surveys, and a thorough examination of recent applications of deep learning and feature fusion for detecting DDoS attacks in IoT networks. Importantly, it highlights the current challenges and limitations of these deep learning techniques based on the literature surveyed. This review concludes by suggesting promising areas for further research to enhance deep learning security solutions, which are specifically tailored to safeguarding the fast-growing IoT infrastructure against DDoS attacks. Mesopotamian Academic Press 2024-04-30 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41709/1/Distributed%20denial%20of%20service%20attack%20Detection%20in%20IoT%20networks.pdf Nuhu Ahmad, Abdul hafiz and Anis Farihan, Mat Raffei and Mohd Faizal, Ab Razak and Ahmad Syafadhli, Abu Bakar (2024) Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review. Mesopotamian Journal of CyberSecurity, 4 (1). pp. 47-70. ISSN 2958-6542. (Published) https://doi.org/10.58496/MJCS/2024/004 https://doi.org/10.58496/MJCS/2024/004 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Nuhu Ahmad, Abdul hafiz Anis Farihan, Mat Raffei Mohd Faizal, Ab Razak Ahmad Syafadhli, Abu Bakar Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review |
| title | Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review |
| title_full | Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review |
| title_fullStr | Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review |
| title_full_unstemmed | Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review |
| title_short | Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review |
| title_sort | distributed denial of service attack detection in iot networks using deep learning and feature fusion : a review |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/41709/1/Distributed%20denial%20of%20service%20attack%20Detection%20in%20IoT%20networks.pdf http://umpir.ump.edu.my/id/eprint/41709/ https://doi.org/10.58496/MJCS/2024/004 https://doi.org/10.58496/MJCS/2024/004 |
| url_provider | http://umpir.ump.edu.my/ |