Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach
Abstract Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of th...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-76016-6 |
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| author | Ahmad Almadhor Ali Altalbe Imen Bouazzi Abdullah Al Hejaili Natalia Kryvinska |
| author_facet | Ahmad Almadhor Ali Altalbe Imen Bouazzi Abdullah Al Hejaili Natalia Kryvinska |
| author_sort | Ahmad Almadhor |
| collection | DOAJ |
| description | Abstract Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats. |
| format | Article |
| id | doaj-art-729a35e4232a43c2b6cda7e8c6f68e06 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-729a35e4232a43c2b6cda7e8c6f68e062025-08-20T02:17:50ZengNature PortfolioScientific Reports2045-23222024-10-0114111610.1038/s41598-024-76016-6Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approachAhmad Almadhor0Ali Altalbe1Imen Bouazzi2Abdullah Al Hejaili3Natalia Kryvinska4Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf UniversityDepartment of Computer Science, Prince Sattam Bin Abdulaziz UniversityDepartment of Industrial Engineering, College of Engineering, King Khalid UniversityFaculty of Computers & Information Technology, Computer Science Department, University of TabukDepartment of Information Management and Business Systems, Faculty of Management, Comenius University in BratislavaAbstract Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats.https://doi.org/10.1038/s41598-024-76016-6Cyber-attacksDistributed Denial of Service (DDoS)Deep LearningIoTFederated Learning |
| spellingShingle | Ahmad Almadhor Ali Altalbe Imen Bouazzi Abdullah Al Hejaili Natalia Kryvinska Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach Scientific Reports Cyber-attacks Distributed Denial of Service (DDoS) Deep Learning IoT Federated Learning |
| title | Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach |
| title_full | Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach |
| title_fullStr | Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach |
| title_full_unstemmed | Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach |
| title_short | Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach |
| title_sort | strengthening network ddos attack detection in heterogeneous iot environment with federated xai learning approach |
| topic | Cyber-attacks Distributed Denial of Service (DDoS) Deep Learning IoT Federated Learning |
| url | https://doi.org/10.1038/s41598-024-76016-6 |
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