FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things
The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated...
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2024-12-01
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| author | Tayyab Rehman Noshina Tariq Farrukh Aslam Khan Shafqat Ur Rehman |
| author_facet | Tayyab Rehman Noshina Tariq Farrukh Aslam Khan Shafqat Ur Rehman |
| author_sort | Tayyab Rehman |
| collection | DOAJ |
| description | The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure. Developing a robust Intrusion Detection System (IDS) addresses the challenges and vulnerabilities present in these systems. Traditional IDS methods have achieved high detection accuracy but need improved scalability and privacy issues from large datasets. This paper proposes a Fog-enabled Federated Learning-based Intrusion Detection System (FFL-IDS) utilizing Convolutional Neural Network (CNN) that mitigates these limitations. This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. The proposed FFL-IDS is validated on two datasets, namely the Edge-IIoTset, explicitly tailored to environments with IIoT, and CIC-IDS2017, comprising various network scenarios. On the Edge-IIoTset dataset, it achieved 93.4% accuracy, 91.6% recall, 88% precision, 87% F1 score, and 87% specificity for jamming and spoofing attacks. The system showed better robustness on the CIC-IDS2017 dataset, achieving 95.8% accuracy, 94.9% precision, 94% recall, 93% F1 score, and 93% specificity. These results establish the proposed framework as a scalable, privacy-preserving, high-performance solution for securing IIoT networks against sophisticated cyber threats across diverse environments. |
| format | Article |
| id | doaj-art-3cd7efcb4ad34a8bbcbb59ad30cf4b65 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3cd7efcb4ad34a8bbcbb59ad30cf4b652025-01-10T13:20:31ZengMDPI AGSensors1424-82202024-12-012511010.3390/s25010010FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of ThingsTayyab Rehman0Noshina Tariq1Farrukh Aslam Khan2Shafqat Ur Rehman3Department of Information Engineering, Computer Science, and Mathematics, University of L’Aquila, 67100 L’Aquila, ItalyDepartment of Artificial Intelligence and Data Science, National University of Computer and Emerging Sciences, Islamabad 44000, PakistanCenter of Excellence in Information Assurance, King Saud University, Riyadh 11653, Saudi ArabiaStony Brook Institute at Anhui University, Hefei 230000, ChinaThe Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure. Developing a robust Intrusion Detection System (IDS) addresses the challenges and vulnerabilities present in these systems. Traditional IDS methods have achieved high detection accuracy but need improved scalability and privacy issues from large datasets. This paper proposes a Fog-enabled Federated Learning-based Intrusion Detection System (FFL-IDS) utilizing Convolutional Neural Network (CNN) that mitigates these limitations. This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. The proposed FFL-IDS is validated on two datasets, namely the Edge-IIoTset, explicitly tailored to environments with IIoT, and CIC-IDS2017, comprising various network scenarios. On the Edge-IIoTset dataset, it achieved 93.4% accuracy, 91.6% recall, 88% precision, 87% F1 score, and 87% specificity for jamming and spoofing attacks. The system showed better robustness on the CIC-IDS2017 dataset, achieving 95.8% accuracy, 94.9% precision, 94% recall, 93% F1 score, and 93% specificity. These results establish the proposed framework as a scalable, privacy-preserving, high-performance solution for securing IIoT networks against sophisticated cyber threats across diverse environments.https://www.mdpi.com/1424-8220/25/1/10convolutional neural networkfederated learningindustrial internet of thingsjamming attackspoofing attacknetwork intrusion detection |
| spellingShingle | Tayyab Rehman Noshina Tariq Farrukh Aslam Khan Shafqat Ur Rehman FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things Sensors convolutional neural network federated learning industrial internet of things jamming attack spoofing attack network intrusion detection |
| title | FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things |
| title_full | FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things |
| title_fullStr | FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things |
| title_full_unstemmed | FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things |
| title_short | FFL-IDS: A Fog-Enabled Federated Learning-Based Intrusion Detection System to Counter Jamming and Spoofing Attacks for the Industrial Internet of Things |
| title_sort | ffl ids a fog enabled federated learning based intrusion detection system to counter jamming and spoofing attacks for the industrial internet of things |
| topic | convolutional neural network federated learning industrial internet of things jamming attack spoofing attack network intrusion detection |
| url | https://www.mdpi.com/1424-8220/25/1/10 |
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