Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
In the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of Service (DDoS) attacks represent a significant challenge, compromising the reliability of these systems. Traditional centralized detection methods struggle to cope effectively in the widespread and diverse envir...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10474358/ |
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| author | Yaser Alhasawi Salem Alghamdi |
| author_facet | Yaser Alhasawi Salem Alghamdi |
| author_sort | Yaser Alhasawi |
| collection | DOAJ |
| description | In the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of Service (DDoS) attacks represent a significant challenge, compromising the reliability of these systems. Traditional centralized detection methods struggle to cope effectively in the widespread and diverse environment of IoT, leading to the exploration of decentralized approaches. This study introduces a Federated Learning-based approach, named Federated Learning for Decentralized DDoS Attack Detection (FL-DAD), which utilizes Convolutional Neural Networks (CNN) to efficiently identify DDoS attacks at the source. Our approach prioritizes data privacy by processing data locally, thereby avoiding the need for central data collection, while enhancing detection efficiency. Evaluated using the comprehensive CICIDS2017 dataset and compared with conventional centralized detection methods, FL-DAD achieves superior performance, illustrating the potential of federated learning to enhance intrusion detection systems in large-scale IoT networks by balancing data security with analytical effectiveness. |
| format | Article |
| id | doaj-art-1701eb466706421cb039c7f016b9daf9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1701eb466706421cb039c7f016b9daf92025-01-25T00:00:18ZengIEEEIEEE Access2169-35362024-01-0112423574236810.1109/ACCESS.2024.337872710474358Federated Learning for Decentralized DDoS Attack Detection in IoT NetworksYaser Alhasawi0https://orcid.org/0000-0003-0396-094XSalem Alghamdi1https://orcid.org/0000-0002-0829-0597King Abdulaziz University (KAU), Jeddah, Saudi ArabiaKing Abdulaziz University (KAU), Jeddah, Saudi ArabiaIn the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of Service (DDoS) attacks represent a significant challenge, compromising the reliability of these systems. Traditional centralized detection methods struggle to cope effectively in the widespread and diverse environment of IoT, leading to the exploration of decentralized approaches. This study introduces a Federated Learning-based approach, named Federated Learning for Decentralized DDoS Attack Detection (FL-DAD), which utilizes Convolutional Neural Networks (CNN) to efficiently identify DDoS attacks at the source. Our approach prioritizes data privacy by processing data locally, thereby avoiding the need for central data collection, while enhancing detection efficiency. Evaluated using the comprehensive CICIDS2017 dataset and compared with conventional centralized detection methods, FL-DAD achieves superior performance, illustrating the potential of federated learning to enhance intrusion detection systems in large-scale IoT networks by balancing data security with analytical effectiveness.https://ieeexplore.ieee.org/document/10474358/Federated learningDDoS attack detectionIoT networksconvolutional neural networksdecentralized intrusion detection |
| spellingShingle | Yaser Alhasawi Salem Alghamdi Federated Learning for Decentralized DDoS Attack Detection in IoT Networks IEEE Access Federated learning DDoS attack detection IoT networks convolutional neural networks decentralized intrusion detection |
| title | Federated Learning for Decentralized DDoS Attack Detection in IoT Networks |
| title_full | Federated Learning for Decentralized DDoS Attack Detection in IoT Networks |
| title_fullStr | Federated Learning for Decentralized DDoS Attack Detection in IoT Networks |
| title_full_unstemmed | Federated Learning for Decentralized DDoS Attack Detection in IoT Networks |
| title_short | Federated Learning for Decentralized DDoS Attack Detection in IoT Networks |
| title_sort | federated learning for decentralized ddos attack detection in iot networks |
| topic | Federated learning DDoS attack detection IoT networks convolutional neural networks decentralized intrusion detection |
| url | https://ieeexplore.ieee.org/document/10474358/ |
| work_keys_str_mv | AT yaseralhasawi federatedlearningfordecentralizedddosattackdetectioniniotnetworks AT salemalghamdi federatedlearningfordecentralizedddosattackdetectioniniotnetworks |