Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis
Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficie...
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PeerJ Inc.
2025-03-01
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| Online Access: | https://peerj.com/articles/cs-2751.pdf |
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| author | Raja Waseem Anwar Mohammad Abrar Abdu Salam Faizan Ullah |
| author_facet | Raja Waseem Anwar Mohammad Abrar Abdu Salam Faizan Ullah |
| author_sort | Raja Waseem Anwar |
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| description | Intrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficiency, and scalability, particularly in resource-constrained IoT environments. This study aims to create and assess a federated learning (FL) framework that integrates with long short-term memory (LSTM) networks for efficient intrusion detection in IoT-based WSNs. We design the framework to enhance detection accuracy, minimize false positive rates (FPR), and ensure data privacy, while maintaining system scalability. Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. The proposed model was tested on three widely used datasets: WSN-DS, CIC-IDS-2017, and UNSW-NB15. The evaluation metrics for its performance included accuracy, F1 score, FPR, and root mean square error (RMSE). We evaluated the performance of the FL-based LSTM model against traditional centralized models, finding significant improvements in intrusion detection. The FL-based LSTM model achieved higher accuracy and a lower FPR across all datasets than centralized models. It effectively managed sequential data in WSNs, ensuring data privacy while maintaining competitive performance, particularly in complex attack scenarios. FL and LSTM networks work well together to make a strong way to find intrusions in IoT-based WSNs, which improves both privacy and detection. This study underscores the potential of FL-based systems to address key challenges in IoT security, including data privacy, scalability, and performance, making the proposed framework suitable for real-world IoT applications. |
| format | Article |
| id | doaj-art-28a7645bd1b84dfe886b991d612e9079 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-03-01 |
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| series | PeerJ Computer Science |
| spelling | doaj-art-28a7645bd1b84dfe886b991d612e90792025-08-20T03:40:48ZengPeerJ Inc.PeerJ Computer Science2376-59922025-03-0111e275110.7717/peerj-cs.2751Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysisRaja Waseem Anwar0Mohammad Abrar1Abdu Salam2Faizan Ullah3Department of Computer Science, German University of Technology in Oman, Muscat, OmanFaculty of Computer Studies, Arab Open University, Muscat, OmanDepartment of Computer Science, Abdul Wali Khan University, Mardan, KPK, PakistanDepartment of Computer Science, Bacha Khan University, Charsadda, KPK, PakistanIntrusion detection in Internet of Things (IoT)-based wireless sensor networks (WSNs) is essential due to their widespread use and inherent vulnerability to security breaches. Traditional centralized intrusion detection systems (IDS) face significant challenges in data privacy, computational efficiency, and scalability, particularly in resource-constrained IoT environments. This study aims to create and assess a federated learning (FL) framework that integrates with long short-term memory (LSTM) networks for efficient intrusion detection in IoT-based WSNs. We design the framework to enhance detection accuracy, minimize false positive rates (FPR), and ensure data privacy, while maintaining system scalability. Using an FL approach, multiple IoT nodes collaboratively train a global LSTM model without exchanging raw data, thereby addressing privacy concerns and improving detection capabilities. The proposed model was tested on three widely used datasets: WSN-DS, CIC-IDS-2017, and UNSW-NB15. The evaluation metrics for its performance included accuracy, F1 score, FPR, and root mean square error (RMSE). We evaluated the performance of the FL-based LSTM model against traditional centralized models, finding significant improvements in intrusion detection. The FL-based LSTM model achieved higher accuracy and a lower FPR across all datasets than centralized models. It effectively managed sequential data in WSNs, ensuring data privacy while maintaining competitive performance, particularly in complex attack scenarios. FL and LSTM networks work well together to make a strong way to find intrusions in IoT-based WSNs, which improves both privacy and detection. This study underscores the potential of FL-based systems to address key challenges in IoT security, including data privacy, scalability, and performance, making the proposed framework suitable for real-world IoT applications.https://peerj.com/articles/cs-2751.pdfFederated learningLSTMIntrusion detectionIoTWireless sensor networksData privacy |
| spellingShingle | Raja Waseem Anwar Mohammad Abrar Abdu Salam Faizan Ullah Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis PeerJ Computer Science Federated learning LSTM Intrusion detection IoT Wireless sensor networks Data privacy |
| title | Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis |
| title_full | Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis |
| title_fullStr | Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis |
| title_full_unstemmed | Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis |
| title_short | Federated learning with LSTM for intrusion detection in IoT-based wireless sensor networks: a multi-dataset analysis |
| title_sort | federated learning with lstm for intrusion detection in iot based wireless sensor networks a multi dataset analysis |
| topic | Federated learning LSTM Intrusion detection IoT Wireless sensor networks Data privacy |
| url | https://peerj.com/articles/cs-2751.pdf |
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