Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review
The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829842/ |
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| author | Tamara Al-Shurbaji Mohammed Anbar Selvakumar Manickam Iznan H Hasbullah Nadia Alfriehat Basim Ahmad Alabsi Ahmad Reda Alzighaibi Hasan Hashim |
| author_facet | Tamara Al-Shurbaji Mohammed Anbar Selvakumar Manickam Iznan H Hasbullah Nadia Alfriehat Basim Ahmad Alabsi Ahmad Reda Alzighaibi Hasan Hashim |
| author_sort | Tamara Al-Shurbaji |
| collection | DOAJ |
| description | The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting and mitigating botnet activities in IoT environments. Therefore, this paper thoroughly reviews existing literature on botnet detection in the IoT using DL-based IDS. It consolidates and analyzes a wide range of research papers, highlighting key findings, methodologies, advancements, shortcomings, and challenges in the field. Additionally, we performed a qualitative comparison with existing surveys using author-defined metrics to underscore the uniqueness of this survey. We also discuss challenges, limitations, and future research directions, emphasizing the distinctive contributions of our review. Ultimately, this survey serves as a guideline for future researchers, contributing to the advancement of botnet detection methods in IoT environments and enhancing security against botnet threats. |
| format | Article |
| id | doaj-art-d0c9c7926b1f451a8b95269c5569b18f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0c9c7926b1f451a8b95269c5569b18f2025-01-24T00:01:41ZengIEEEIEEE Access2169-35362025-01-0113117921182210.1109/ACCESS.2025.352671110829842Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A ReviewTamara Al-Shurbaji0https://orcid.org/0000-0003-3215-6862Mohammed Anbar1https://orcid.org/0000-0002-7026-6408Selvakumar Manickam2https://orcid.org/0000-0003-4378-1954Iznan H Hasbullah3https://orcid.org/0000-0002-2275-3201Nadia Alfriehat4Basim Ahmad Alabsi5Ahmad Reda Alzighaibi6Hasan Hashim7National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Minden Heights, Pinang, MalaysiaNational Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Minden Heights, Pinang, MalaysiaNational Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Minden Heights, Pinang, MalaysiaNational Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Minden Heights, Pinang, MalaysiaNational Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Minden Heights, Pinang, MalaysiaApplied College, Najran University, Najran, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Engineering, Taibah University, Madinah, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Engineering, Taibah University, Madinah, Saudi ArabiaThe proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting and mitigating botnet activities in IoT environments. Therefore, this paper thoroughly reviews existing literature on botnet detection in the IoT using DL-based IDS. It consolidates and analyzes a wide range of research papers, highlighting key findings, methodologies, advancements, shortcomings, and challenges in the field. Additionally, we performed a qualitative comparison with existing surveys using author-defined metrics to underscore the uniqueness of this survey. We also discuss challenges, limitations, and future research directions, emphasizing the distinctive contributions of our review. Ultimately, this survey serves as a guideline for future researchers, contributing to the advancement of botnet detection methods in IoT environments and enhancing security against botnet threats.https://ieeexplore.ieee.org/document/10829842/Intrusion detection system (IDS)botnetdeep learningInternet of Things (IoT)IoT Botnetneural networks |
| spellingShingle | Tamara Al-Shurbaji Mohammed Anbar Selvakumar Manickam Iznan H Hasbullah Nadia Alfriehat Basim Ahmad Alabsi Ahmad Reda Alzighaibi Hasan Hashim Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review IEEE Access Intrusion detection system (IDS) botnet deep learning Internet of Things (IoT) IoT Botnet neural networks |
| title | Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review |
| title_full | Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review |
| title_fullStr | Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review |
| title_full_unstemmed | Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review |
| title_short | Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review |
| title_sort | deep learning based intrusion detection system for detecting iot botnet attacks a review |
| topic | Intrusion detection system (IDS) botnet deep learning Internet of Things (IoT) IoT Botnet neural networks |
| url | https://ieeexplore.ieee.org/document/10829842/ |
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