Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
The escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have sh...
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-12-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
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| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31919 |
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| author | Marcos Luengo Viñuela Jesús-Ángel Román Gallego |
| author_facet | Marcos Luengo Viñuela Jesús-Ángel Román Gallego |
| author_sort | Marcos Luengo Viñuela |
| collection | DOAJ |
| description | The escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have shown efficacy in this realm. This study conducts a systematic review of literature from 2018 to 2023, focusing on DDoS attack detection in IoT Networks using deep learning techniques. Employing the PRISMA methodology, the review identifies and evaluates studies, synthesizing key findings/2**. It highlights that incorporating deep learning significantly enhances DDoS attack detection precision and efficiency, achieving detection rates between 94 % and 99 %. Despite progress, challenges persist, such as limited training data and IoT device processing constraints with large data volumes. This review underscores the importance of addressing these challenges to improve DDoS attack detection in IoT Networks. The research's significance lies in IoT's growing importance and security concerns. It contributes by showcasing current state-of-the-art DDoS detection through deep learning while outlining persistent challenges. Recognizing deep learning's effectiveness sets the stage for refining IoT security protocols, and moreover, by identifying challenges, the research informs strategies to enhance IoT security, fostering a resilient framework. |
| format | Article |
| id | doaj-art-a5925fabf551408990ac4250c4715ef7 |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-a5925fabf551408990ac4250c4715ef72025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-12-0113e31919e3191910.14201/adcaij.3191937400Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT NetworksMarcos Luengo Viñuela0Jesús-Ángel Román Gallego1Polytechnic School of Zamora, Computer Sciences Department, University of Salamanca, Avda. Cardenal Cisneros, 34, Zamora, 49022Polytechnic School of Zamora, Computer Sciences Department, University of Salamanca, Avda. Cardenal Cisneros, 34, Zamora, 49022The escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have shown efficacy in this realm. This study conducts a systematic review of literature from 2018 to 2023, focusing on DDoS attack detection in IoT Networks using deep learning techniques. Employing the PRISMA methodology, the review identifies and evaluates studies, synthesizing key findings/2**. It highlights that incorporating deep learning significantly enhances DDoS attack detection precision and efficiency, achieving detection rates between 94 % and 99 %. Despite progress, challenges persist, such as limited training data and IoT device processing constraints with large data volumes. This review underscores the importance of addressing these challenges to improve DDoS attack detection in IoT Networks. The research's significance lies in IoT's growing importance and security concerns. It contributes by showcasing current state-of-the-art DDoS detection through deep learning while outlining persistent challenges. Recognizing deep learning's effectiveness sets the stage for refining IoT security protocols, and moreover, by identifying challenges, the research informs strategies to enhance IoT security, fostering a resilient framework.https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31919ddosiot networksaicybersecurityslrmlmachine learning |
| spellingShingle | Marcos Luengo Viñuela Jesús-Ángel Román Gallego Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks Advances in Distributed Computing and Artificial Intelligence Journal ddos iot networks ai cybersecurity slr ml machine learning |
| title | Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks |
| title_full | Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks |
| title_fullStr | Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks |
| title_full_unstemmed | Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks |
| title_short | Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks |
| title_sort | systematic literature review of machine learning models for detecting ddos attacks in iot networks |
| topic | ddos iot networks ai cybersecurity slr ml machine learning |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31919 |
| work_keys_str_mv | AT marcosluengovinuela systematicliteraturereviewofmachinelearningmodelsfordetectingddosattacksiniotnetworks AT jesusangelromangallego systematicliteraturereviewofmachinelearningmodelsfordetectingddosattacksiniotnetworks |