PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things
Network Intrusion Detection Systems (NIDS) play a crucial role in IoT security. In recent years, deep learning-based intrusion detection systems have demonstrated excellent performance. However, the high computational and storage requirements make these impractical for most IoT devices. To address t...
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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/11020677/ |
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| author | Auwal Sani Iliyasu Abdul Jabbar Siddiqui Houbing Song Fahad Jibrin Abdu |
| author_facet | Auwal Sani Iliyasu Abdul Jabbar Siddiqui Houbing Song Fahad Jibrin Abdu |
| author_sort | Auwal Sani Iliyasu |
| collection | DOAJ |
| description | Network Intrusion Detection Systems (NIDS) play a crucial role in IoT security. In recent years, deep learning-based intrusion detection systems have demonstrated excellent performance. However, the high computational and storage requirements make these impractical for most IoT devices. To address this pressing issue, we propose PNet-IDS, a novel lightweight convolutional neural network (CNN)-based method to reduce computational complexity and optimize on-device resource usage for real-time intrusion detection. The key contribution of the proposed method is the reduced number of floating point operations (FLOPs) and effective utilization of on-device computational resources at high accuracies and precision, making PNet-IDS lightweight and efficient for real-time next generation IoT intrusion detection. Moreover, PNet-IDS’ robustness against distribution shifts in network traffic is enhanced by through a knowledge distillation framework. Comprehensive experimental evaluations using the popular BoT-IoT and CIC-IDS2017 benchmark datasets prove the superiority of the proposed PNet-IDS over competitive related methods in terms of reduced parameters count, reduced FLOPs, reduced model size while maintaining high accuracy and precision. By combining PNet-IDS’ efficiency with knowledge distillation’s adaptability, the proposed method offers a scalable and resilient solution for IoT intrusion detection. |
| format | Article |
| id | doaj-art-cf74d64e90174a878aa9a698a478aaee |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cf74d64e90174a878aa9a698a478aaee2025-08-20T02:07:31ZengIEEEIEEE Access2169-35362025-01-011310262410263910.1109/ACCESS.2025.357570511020677PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of ThingsAuwal Sani Iliyasu0Abdul Jabbar Siddiqui1https://orcid.org/0000-0001-6233-598XHoubing Song2https://orcid.org/0000-0003-2631-9223Fahad Jibrin Abdu3https://orcid.org/0000-0002-4942-1258Interdisciplinary Research Center for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaInterdisciplinary Research Center for Intelligent Secure Systems, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaDepartment of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USASDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi ArabiaNetwork Intrusion Detection Systems (NIDS) play a crucial role in IoT security. In recent years, deep learning-based intrusion detection systems have demonstrated excellent performance. However, the high computational and storage requirements make these impractical for most IoT devices. To address this pressing issue, we propose PNet-IDS, a novel lightweight convolutional neural network (CNN)-based method to reduce computational complexity and optimize on-device resource usage for real-time intrusion detection. The key contribution of the proposed method is the reduced number of floating point operations (FLOPs) and effective utilization of on-device computational resources at high accuracies and precision, making PNet-IDS lightweight and efficient for real-time next generation IoT intrusion detection. Moreover, PNet-IDS’ robustness against distribution shifts in network traffic is enhanced by through a knowledge distillation framework. Comprehensive experimental evaluations using the popular BoT-IoT and CIC-IDS2017 benchmark datasets prove the superiority of the proposed PNet-IDS over competitive related methods in terms of reduced parameters count, reduced FLOPs, reduced model size while maintaining high accuracy and precision. By combining PNet-IDS’ efficiency with knowledge distillation’s adaptability, the proposed method offers a scalable and resilient solution for IoT intrusion detection.https://ieeexplore.ieee.org/document/11020677/Deep learningconvolutional neural network (CNN)Internet of Things (IoT)knowledge distillation (KD)lightweight modelsnetwork intrusion detection system (NIDS) |
| spellingShingle | Auwal Sani Iliyasu Abdul Jabbar Siddiqui Houbing Song Fahad Jibrin Abdu PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things IEEE Access Deep learning convolutional neural network (CNN) Internet of Things (IoT) knowledge distillation (KD) lightweight models network intrusion detection system (NIDS) |
| title | PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things |
| title_full | PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things |
| title_fullStr | PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things |
| title_full_unstemmed | PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things |
| title_short | PNet-IDS: A Lightweight and Generalizable Convolutional Neural Network for Intrusion Detection in Internet of Things |
| title_sort | pnet ids a lightweight and generalizable convolutional neural network for intrusion detection in internet of things |
| topic | Deep learning convolutional neural network (CNN) Internet of Things (IoT) knowledge distillation (KD) lightweight models network intrusion detection system (NIDS) |
| url | https://ieeexplore.ieee.org/document/11020677/ |
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