Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach
The Internet of Things (IoT) has revolutionized the world with its diverse applications and smart connected devices. These IoT devices communicate with each other without human intervention and make life easier in many ways. However, the independence of these devices raises several significant conce...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10147216/ |
| _version_ | 1827921439024480256 |
|---|---|
| author | Kamran Ahmad Awan Ikram Ud Din Mahdi Zareei Ahmad Almogren Byung Seo-Kim Jesus Arturo Perez-Diaz |
| author_facet | Kamran Ahmad Awan Ikram Ud Din Mahdi Zareei Ahmad Almogren Byung Seo-Kim Jesus Arturo Perez-Diaz |
| author_sort | Kamran Ahmad Awan |
| collection | DOAJ |
| description | The Internet of Things (IoT) has revolutionized the world with its diverse applications and smart connected devices. These IoT devices communicate with each other without human intervention and make life easier in many ways. However, the independence of these devices raises several significant concerns, such as security and privacy preservation due to malicious and compromised nodes within the network. Trust management has been introduced as a less computationally intensive alternative to traditional approaches such as cryptography. The proposed FedTrust approach addresses these challenges by designing a method for identifying malicious and compromised nodes using federated learning. FedTrust trains edge nodes with a provided dataset and forms a global model to predict the abnormal behavior of IoT nodes. The proposed approach utilizes a novel trust dataset consisting of 19 trust parameters from three major components: knowledge, experience, and reputation. To reduce the computational burden, FedTrust employs the concept of communities with dedicated servers to divide the dataset into smaller parts for more efficient training. The proposed approach is extensively evaluated in comparison to existing approaches in terms of accuracy, precision, and other metrics to validate its performance in IoT networks. Simulation results demonstrate the effectiveness of FedTrust by achieving a higher rate of detection and prediction of malicious and compromised nodes. |
| first_indexed | 2024-03-13T04:27:34Z |
| format | Article |
| id | doaj.art-1b6acbf99c5c499a81c6398fa50f3c87 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T04:27:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b6acbf99c5c499a81c6398fa50f3c872023-06-19T23:00:35ZengIEEEIEEE Access2169-35362023-01-0111589015891410.1109/ACCESS.2023.328467710147216Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification ApproachKamran Ahmad Awan0https://orcid.org/0000-0002-0038-3772Ikram Ud Din1https://orcid.org/0000-0001-8896-547XMahdi Zareei2https://orcid.org/0000-0001-6623-1758Ahmad Almogren3https://orcid.org/0000-0002-8253-9709Byung Seo-Kim4https://orcid.org/0000-0001-9824-1950Jesus Arturo Perez-Diaz5https://orcid.org/0000-0002-7678-5487Department of Information Technology, The University of Haripur, Haripur, PakistanDepartment of Information Technology, The University of Haripur, Haripur, PakistanTecnologico de Monterrey, School of Engineering and Sciences, Zapopan, MexicoDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Software and Communications Engineering, Hongik University, Sejong, South KoreaTecnologico de Monterrey, School of Engineering and Sciences, Zapopan, MexicoThe Internet of Things (IoT) has revolutionized the world with its diverse applications and smart connected devices. These IoT devices communicate with each other without human intervention and make life easier in many ways. However, the independence of these devices raises several significant concerns, such as security and privacy preservation due to malicious and compromised nodes within the network. Trust management has been introduced as a less computationally intensive alternative to traditional approaches such as cryptography. The proposed FedTrust approach addresses these challenges by designing a method for identifying malicious and compromised nodes using federated learning. FedTrust trains edge nodes with a provided dataset and forms a global model to predict the abnormal behavior of IoT nodes. The proposed approach utilizes a novel trust dataset consisting of 19 trust parameters from three major components: knowledge, experience, and reputation. To reduce the computational burden, FedTrust employs the concept of communities with dedicated servers to divide the dataset into smaller parts for more efficient training. The proposed approach is extensively evaluated in comparison to existing approaches in terms of accuracy, precision, and other metrics to validate its performance in IoT networks. Simulation results demonstrate the effectiveness of FedTrust by achieving a higher rate of detection and prediction of malicious and compromised nodes.https://ieeexplore.ieee.org/document/10147216/Internet of Thingsfederated learningtrust managementdeep learningmalicious nodessecurity |
| spellingShingle | Kamran Ahmad Awan Ikram Ud Din Mahdi Zareei Ahmad Almogren Byung Seo-Kim Jesus Arturo Perez-Diaz Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach IEEE Access Internet of Things federated learning trust management deep learning malicious nodes security |
| title | Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach |
| title_full | Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach |
| title_fullStr | Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach |
| title_full_unstemmed | Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach |
| title_short | Securing IoT With Deep Federated Learning: A Trust-Based Malicious Node Identification Approach |
| title_sort | securing iot with deep federated learning a trust based malicious node identification approach |
| topic | Internet of Things federated learning trust management deep learning malicious nodes security |
| url | https://ieeexplore.ieee.org/document/10147216/ |
| work_keys_str_mv | AT kamranahmadawan securingiotwithdeepfederatedlearningatrustbasedmaliciousnodeidentificationapproach AT ikramuddin securingiotwithdeepfederatedlearningatrustbasedmaliciousnodeidentificationapproach AT mahdizareei securingiotwithdeepfederatedlearningatrustbasedmaliciousnodeidentificationapproach AT ahmadalmogren securingiotwithdeepfederatedlearningatrustbasedmaliciousnodeidentificationapproach AT byungseokim securingiotwithdeepfederatedlearningatrustbasedmaliciousnodeidentificationapproach AT jesusarturoperezdiaz securingiotwithdeepfederatedlearningatrustbasedmaliciousnodeidentificationapproach |