Smart City Gnosys
Smart city article details
| Title | Adaptive Cyber Defence: Leveraging Gans For Simulating And Mitigating Advanced Network Attacks In Iot Environments |
|---|---|
| ID_Doc | 6227 |
| Authors | Rao P.K.; Chatterjee S.; Prakash P.S.; Ramana K.S. |
| Year | 2025 |
| Published | Lecture Notes in Networks and Systems, 980 LNNS |
| DOI | http://dx.doi.org/10.1007/978-981-97-9762-2_19 |
| Abstract | The progressive dissemination of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has ushered in a new era of connectivity, with vast applications spanning from medicare to smart city infrastructure. However, this expansion has been paralleled by a corresponding increase in the sophistication and variety of cyber threats targeting these networks. Traditional cyber security measures, designed for a less dynamic threat landscape, are proving increasingly insufficient in protecting against the innovative and varied attack methods now in commonplace. This study introduces an innovative application of Generative Adversarial Networks (GANs) to address this challenge, presenting a novel framework for the simulation and mitigation of advanced network attacks, particularly focusing on Distributed Denial of Service (DDoS) and spoofing attacks which pose significant threats in IoT environments. Generative Adversarial Networks (GANs), comprising two neural networks-the generator and the discriminator compete in a game-theoretic scenario, facilitating a deep understanding of attack patterns through the generation of realistic, synthetic cyber-attack scenarios. This research exploits GANs to bridge the gap between the static nature of traditional security protocols and the dynamic, evolving landscape of cyber threats. By training on a comprehensive dataset of known attacks and normal network activities, our proposed model, the Dynamic Adaptive Threat Simulation GAN (DATS-GAN), is capable of producing varied and realistic attack scenarios. These simulations serve a dual purpose: they not only enhance the detection capabilities and responsiveness of current security systems but also provide a basis for the development of new, adaptive security mechanisms capable of dynamically responding to the ever-changing cyber threat landscape. The effectiveness of DATS-GAN is demonstrated through extensive empirical analysis, highlighting significant improvements in the detection precision and reaction times of security frameworks within WSNs. Moreover, the generated synthetic attack scenarios provide a valuable resource for training machine learning models, leading to the advancement of adaptive security solutions that maintain a high readiness level against emerging cyber threats. The outcomes of this research hold substantial promise for the cyber security domain, showcasing the potential of GANs to revolutionize network defenses against sophisticated cyber threats in IoT and WSN environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. |
| Author Keywords | Adaptive security mechanisms; AI in cyber defence; Cybersecurity in WSN; DDoS attack mitigation; Generative Adversarial Networks (GANs); IoT security; Network attack simulation |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
36207  | 0.922 | Bethu S. | Malicious Attack Detection In Iot By Generative Adversarial Networks | SN Computer Science, 6, 4 (2025) |
| 23933 | 0.898 | Ponnusamy S.; Antari J.; Bhaladhare P.; Potgantwar A.; Kalyanaraman S. | Enhancing Security In Public Spaces Through Generative Adversarial Networks (Gans) | Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs) (2024) |
| 6670 | 0.886 | 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) |
| 29764 | 0.883 | 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) |
| 29736 | 0.882 | 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) |
| 55625 | 0.88 | 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) |
| 2169 | 0.88 | 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) |
| 23675 | 0.875 | Sirigineedi M.; Manke A.; Verma S.; Baskar K. | Enhanced Security In Smart City Gan-Based Intrusion Detection Systems In Wsns | Enhancing Security in Public Spaces Through Generative Adversarial Networks (GANs) (2024) |
| 6741 | 0.87 | Rashid M.M.; Kamruzzaman J.; Mehedi Hassan M.; Imam T.; Wibowo S.; Gordon S.; Fortino G. | Adversarial Training For Deep Learning-Based Cyberattack Detection In Iot-Based Smart City Applications | Computers and Security, 120 (2022) |
| 47817 | 0.866 | Pitta S.; Gopalakrishnan S.; Chand S.R. | Securing Wsn-Iot Networks Using Swinalert-Gan: A Deep Learning-Based Intrusion Detection Framework | Proceedings of 3rd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2025 (2025) |