Smart City Gnosys
Smart city article details
| Title | A Graphics Processing Unit Assisted Cnn-Gru Framework For The Intrusion Detection Mechanism In The Industrial Internet Of Things |
|---|---|
| ID_Doc | 1993 |
| Authors | Chithra Rani P.R.; Baalaji K. |
| Year | 2025 |
| Published | Engineering Research Express, 7, 2 |
| DOI | http://dx.doi.org/10.1088/2631-8695/adc971 |
| Abstract | In recent years, there has been a remarkable surge in cyber-attacks and security breaches targeting the Internet of Things (IoT) and Industrial IoT (IIoTs). In today’s computing world, safeguarding the IIoT network and its underlying architecture has become a top priority. Given the nature of sensor networks and the communication environment, it is often difficult to identify and thwart undesired network activity. In today’s networks, network intrusion detection systems are being implemented to detect various harmful activities that may be performed. To begin with, an innovative network intrusion detection (ID) model has been addressed to enhance the problems posed by existing intrusion detection models. In this work, an advanced hybrid structure model that integrates Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed for identifying botnets and cyber-attacks in industrial sensors and camera networks. In addition, this architecture addresses the weaknesses of traditional intrusion detection systems, such as limited computational capacity and the requirement for highly expansive computing platforms to deploy real-time models. Consequently, a framework for multiclass intrusion is developed and tested in the NVIDIA Jetson Nano Graphic Processing Unit (GPU) computer. The performance analysis of the developed model was conducted using various datasets and compared with CNN and GRU-assisted networks. The CNN-GRU framework demonstrates the best performance for accuracy, at 89.46%; precision, at 95.67%; recall, at 90.82%; F1 score, at 88.07%; weighted average (0.96); and macro average (0.94). These results also emphasized the importance of a hybrid software model combined with GPU hardware to ensure maximum intrusion detection performance for IIoT networks, including smart cities and smart homes. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. |
| Author Keywords | botnet; cyber-attacks; DDoS; deep learning; gated recurrent unit; GPU; intrusion detection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
61984  | 0.886 | Sagar A.; Anushkannan N.K.; Indumathi G.; Vasant Muralidhar N.; Dhamotharan K.A.; Malini P. | Wireless Sensor Network-Based Intrusion Detection Technique Using Deep Learning Approach Of Cnn-Gru | Proceedings of the 8th International Conference on Communication and Electronics Systems, ICCES 2023 (2023) |
| 2169 | 0.878 | Alzahrani M.; Nanda P.; Mohanty M.; El Zegil F. | A Hybrid Gan And Gcn Model For Intrusion Detection In Heterogenous Iot Networks | 2025 17th International Conference on Computer and Automation Engineering, ICCAE 2025 (2025) |
| 957 | 0.873 | Houichi M.; Jaidi F.; Bouhoula A. | A Comprehensive Study Of Intrusion Detection Within Internet Of Things-Based Smart Cities: Synthesis, Analysis And A Novel Approach | 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 (2023) |
| 30732 | 0.87 | Amine M.S.; Nada F.A.; Hosny K.M. | Improved Model For Intrusion Detection In The Internet Of Things | Scientific Reports, 15, 1 (2025) |
| 22889 | 0.867 | Tariq U.; Ahanger T.A. | Employing Sae-Gru Deep Learning For Scalable Botnet Detection In Smart City Infrastructure | PeerJ Computer Science, 11 (2025) |
| 2052 | 0.865 | Elsayed R.; Hamada R.; Hammoudeh M.; Abdalla M.; Elsaid S.A. | A Hierarchical Deep Learning-Based Intrusion Detection Architecture For Clustered Internet Of Things | Journal of Sensor and Actuator Networks, 12, 1 (2023) |
| 3390 | 0.861 | Gupta B.B.; Chui K.T.; Gaurav A.; Arya V.; Chaurasia P. | A Novel Hybrid Convolutional Neural Network- And Gated Recurrent Unit-Based Paradigm For Iot Network Traffic Attack Detection In Smart Cities | Sensors (Basel, Switzerland), 23, 21 (2023) |
| 9648 | 0.86 | Alsoufi M.A.; Razak S.; Siraj M.M.; Nafea I.; Ghaleb F.A.; Saeed F.; Nasser M. | Anomaly-Based Intrusion Detection Systems In Iot Using Deep Learning: A Systematic Literature Review | Applied Sciences (Switzerland), 11, 18 (2021) |
| 33032 | 0.859 | Dawoud A.; Sianaki O.A.; Shahristani S.; Raun C. | Internet Of Things Intrusion Detection: A Deep Learning Approach | 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 (2020) |
| 29764 | 0.859 | Bose S.; Maheswaran N.; Gokulraj G.; Anitha T.; Shruthi T.; Vijayaraj G. | Hybrid Intrusion Detection System For Iot Against Adversarial Threats Using Intelligent Rdls Model | Proceedings of the 5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024 (2024) |