Smart City Gnosys
Smart city article details
| Title | Block Chain Fostered Cycle-Consistent Generative Adversarial Network Framework Espoused Intrusion Detection For Protecting Iot Network |
|---|---|
| ID_Doc | 12337 |
| Authors | Sugitha G.; Solairaj A.; Suresh J. |
| Year | 2022 |
| Published | Transactions on Emerging Telecommunications Technologies, 33, 11 |
| DOI | http://dx.doi.org/10.1002/ett.4578 |
| Abstract | In smart city infrastructure, IoT networks contain intelligent devices for collecting and processing data using open channel internet. Some challenges have occurred in the existing methods while transferring the data, like centralism, safety, secrecy (data destroying, inference attacks), transparency, scalability, verification, and controlling the rapid adaptation of smart cities. To overcome these challenges, a machine learning based block chain method is proposed in this manuscript. The machine learning strategies can process massive datasets. Furthermore, they contain adequate generalization to identify various attack vectors. Here, the block chain fostered cycle-consistent generative adversarial network (CCGAN) framework espoused intrusion detection is proposed for protecting the IoT network. Also, a 3 level privacy model is introduced for protecting the IoT devices. The first level is block chain based privacy detection and the second level is CCGAN and the third level is classification. In first level, ToN-IoT, BoT-IoT datasets are taken to detect the IoT intrusion, these data's are given to the block chain to authenticate and to collect the data in the IoT devices in the smart cities and stored in the blocks present in the block chain. In second level, the feature mapping and feature selection are done. The normal and attacked instances are classified in level 3. The performance of the proposed method shows higher accuracy 25.37%, 29.57%, and 18.67%, higher recall 23.75%, 17.58%, and 14.68% better than the existing methods, like block chain and machine learning method based privacy protection in IoT using optimized gradient tree boosting system (IOT-BC-XGBoost), and block chain and machine learning method based privacy protection in IoT using deep gated recurrent neural network (IOT-BC-DGRNN), respectively. © 2022 John Wiley & Sons, Ltd. |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
53223  | 0.905 | W A.; Brabin D.R.D.; Kumar K.K.; Sunitha T. | Strengthening Security In Iot-Based Smart Cities Utilizing Cycle-Consistent Generative Adversarial Networks For Attack Detection And Secure Data Transmission | Peer-to-Peer Networking and Applications, 18, 2 (2025) |
| 27812 | 0.884 | Alabdulwahab S.; Kim Y.-T.; Seo A.; Son Y. | Generating Synthetic Dataset For Ml-Based Ids Using Ctgan And Feature Selection To Protect Smart Iot Environments | Applied Sciences (Switzerland), 13, 19 (2023) |
| 6670 | 0.883 | Zhukabayeva T.; Benkhelifa E.; Satybaldina D.; Rehman A.U. | Advancing Iot Security: A Review Of Intrusion Detection Systems Challenges And Emerging Solutions | 2024 11th International Conference on Software Defined Systems, SDS 2024 (2024) |
| 35041 | 0.882 | Moawad M.M.; Madbouly M.M.; Guirguis S.K. | Leveraging Blockchain And Machine Learning To Improve Iot Security For Smart Cities | Lecture Notes on Data Engineering and Communications Technologies, 164 (2023) |
| 29736 | 0.881 | Balaji S.; Sankaranarayanan S. | Hybrid Deep-Generative Adversarial Network Based Intrusion Detection Model For Internet Of Things Using Binary Particle Swarm Optimization | International Journal of Electrical and Electronics Research, 10, 4 (2022) |
| 2219 | 0.871 | Alotaibi J. | A Hybrid Software-Defined Networking Approach For Enhancing Iot Cybersecurity With Deep Learning And Blockchain In Smart Cities | Peer-to-Peer Networking and Applications, 18, 3 (2025) |
| 36207 | 0.871 | Bethu S. | Malicious Attack Detection In Iot By Generative Adversarial Networks | SN Computer Science, 6, 4 (2025) |
| 55625 | 0.869 | Thota M.K.; Prathibhavani P.M.; Venugopal K.R. | The Graph Neural Network With Wasserstein Generative Adversarial Network For Botnet Detection In Smart City Iot | 2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024 (2024) |
| 40871 | 0.869 | Villegas-Ch W.; Govea J.; Gutierrez R.; Mera-Navarrete A. | Optimizing Security In Iot Ecosystems Using Hybrid Artificial Intelligence And Blockchain Models: A Scalable And Efficient Approach For Threat Detection | IEEE Access, 13 (2025) |
| 29764 | 0.867 | 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) |