A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones
Underwater drones are vital for scientific research, environmental monitoring, and maritime operations, allowing data collection in challenging environments. However, their deployment faces issues such as low bandwidth, high latency, signal attenuation, and intermittent connectivity due to mobility...
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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/10843690/ |
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| author | Mansahaj Singh Popli Rudra Pratap Singh Navneet Kaur Popli Mohammad Mamun |
| author_facet | Mansahaj Singh Popli Rudra Pratap Singh Navneet Kaur Popli Mohammad Mamun |
| author_sort | Mansahaj Singh Popli |
| collection | DOAJ |
| description | Underwater drones are vital for scientific research, environmental monitoring, and maritime operations, allowing data collection in challenging environments. However, their deployment faces issues such as low bandwidth, high latency, signal attenuation, and intermittent connectivity due to mobility and water currents. Traditional centralized data processing approaches are inefficient under these conditions as they require transmitting large volumes of raw data to a central location. To address these challenges, this study proposes a Federated Learning (FL) framework specifically tailored for underwater networks. Unlike centralized approaches, FL enables underwater drones to collaboratively train a global intrusion detection model by processing data locally and sharing only model updates with the central server. This approach significantly improves data security by ensuring that sensitive information never leaves the local devices, reducing the risk of interception or compromise during transmission. Furthermore, FL’s decentralized architectures inherently aligns with the dynamic and distributed nature of underwater drone networks. The proposed framework improves cyber intrusion detection by leveraging localized insights from individual drones to detect threats, including zero-day attacks, without directly exposing sensitive data. By preserving privacy and enabling collaborative anomaly detection, FL addresses key cybersecurity challenges in the Internet of Underwater Things (IoUT). |
| format | Article |
| id | doaj-art-29dd85a3a475475eb46067566aefe7da |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-29dd85a3a475475eb46067566aefe7da2025-01-25T00:01:37ZengIEEEIEEE Access2169-35362025-01-0113126341264610.1109/ACCESS.2025.353049910843690A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater DronesMansahaj Singh Popli0https://orcid.org/0009-0009-4495-9188Rudra Pratap Singh1https://orcid.org/0000-0002-9642-7679Navneet Kaur Popli2https://orcid.org/0009-0004-8040-0721Mohammad Mamun3https://orcid.org/0000-0002-4045-8687Faculty of Electrical and Computer Engineering, University of Victoria, Victoria, BC, CanadaFaculty of Electrical and Computer Engineering, University of Victoria, Victoria, BC, CanadaFaculty of Electrical and Computer Engineering, University of Victoria, Victoria, BC, CanadaNational Research Council Canada, Fredericton, NB, CanadaUnderwater drones are vital for scientific research, environmental monitoring, and maritime operations, allowing data collection in challenging environments. However, their deployment faces issues such as low bandwidth, high latency, signal attenuation, and intermittent connectivity due to mobility and water currents. Traditional centralized data processing approaches are inefficient under these conditions as they require transmitting large volumes of raw data to a central location. To address these challenges, this study proposes a Federated Learning (FL) framework specifically tailored for underwater networks. Unlike centralized approaches, FL enables underwater drones to collaboratively train a global intrusion detection model by processing data locally and sharing only model updates with the central server. This approach significantly improves data security by ensuring that sensitive information never leaves the local devices, reducing the risk of interception or compromise during transmission. Furthermore, FL’s decentralized architectures inherently aligns with the dynamic and distributed nature of underwater drone networks. The proposed framework improves cyber intrusion detection by leveraging localized insights from individual drones to detect threats, including zero-day attacks, without directly exposing sensitive data. By preserving privacy and enabling collaborative anomaly detection, FL addresses key cybersecurity challenges in the Internet of Underwater Things (IoUT).https://ieeexplore.ieee.org/document/10843690/Internet of Underwater Things (IoUT)Underwater DronesFederated Learning ModelCollaborative Intrusion DetectionDDoS attackCyber-physical System (CPS) |
| spellingShingle | Mansahaj Singh Popli Rudra Pratap Singh Navneet Kaur Popli Mohammad Mamun A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones IEEE Access Internet of Underwater Things (IoUT) Underwater Drones Federated Learning Model Collaborative Intrusion Detection DDoS attack Cyber-physical System (CPS) |
| title | A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones |
| title_full | A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones |
| title_fullStr | A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones |
| title_full_unstemmed | A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones |
| title_short | A Federated Learning Framework for Enhanced Data Security and Cyber Intrusion Detection in Distributed Network of Underwater Drones |
| title_sort | federated learning framework for enhanced data security and cyber intrusion detection in distributed network of underwater drones |
| topic | Internet of Underwater Things (IoUT) Underwater Drones Federated Learning Model Collaborative Intrusion Detection DDoS attack Cyber-physical System (CPS) |
| url | https://ieeexplore.ieee.org/document/10843690/ |
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