Smart City Gnosys
Smart city article details
| Title | Ai-Driven Smart Contract Optimization For Scalable, Real-Time Threat Detection In Decentralized Iot Networks |
|---|---|
| ID_Doc | 7041 |
| Authors | Upadhyaya N.; Seelam D.R.; Agarwal C.; Joseph S.; Joshi H. |
| Year | 2025 |
| Published | Lecture Notes in Networks and Systems, 1312 LNNS |
| DOI | http://dx.doi.org/10.1007/978-3-031-94620-2_27 |
| Abstract | The swift expansion of Internet of Things (IoT) devices has resulted in heightened susceptibility to advanced cyber threats. Traditional centralized security models are no longer sufficient to handle the growing complexity, scale, and dynamic nature of modern IoT environments. This paper proposes a novel framework that integrates Artificial Intelligence (AI) with blockchain-based smart contracts to provide a scalable, decentralized, and real-time threat detection and response system for IoT networks. Our model leverages federated learning, enabling IoT devices to collaboratively detect security anomalies while preserving data privacy, and adaptive AI-driven smart contracts that autonomously adjust security policies in response to evolving threats. The proposed system also addresses critical challenges such as energy efficiency and scalability by incorporating lightweight consensus mechanisms and energy-aware decision-making for IoT devices. Furthermore, our framework facilitates cross-domain threat intelligence sharing through blockchain-based smart contracts, enabling multiple IoT networks to collaborate in detecting and mitigating threats across different industries, such as smart cities and healthcare IoT. This paper presents the theoretical underpinnings of the framework, detailing the architecture and expected performance improvements in detection accuracy, response time, and energy consumption. We hypothesize that the proposed system will outperform existing centralized solutions in terms of scalability, privacy protection, and real-time adaptability, offering a promising avenue for securing IoT networks in a decentralized manner. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. |
| Author Keywords | Adaptive Threat Detection; AI-driven IoT Security; Blockchain in IoT; Cross-Domain Threat Intelligence; Decentralized Security; Federated Learning; Zero-Day Attack Detection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
40871  | 0.917 | 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) |
| 31926 | 0.902 | Khan B.U.I.; Goh K.W.; Khan A.R.; Zuhairi M.F.; Chaimanee M. | Integrating Ai And Blockchain For Enhanced Data Security In Iot-Driven Smart Cities | Processes, 12, 9 (2024) |
| 12575 | 0.899 | Goundar S. | Blockchain-Ai Integration For Resilient Real-Time Cyber Security | Global Congress on Emerging Technologies, GCET 2024 (2024) |
| 4502 | 0.898 | Ayub K.; Alshawa R. | A Secure Iot Framework For Smart Cities: Integrating Servicenow Irm/Grc With Blockchain And Ai-Driven Threat Detection | 2024 International Conference on Computer and Applications, ICCA 2024 (2024) |
| 31949 | 0.894 | Alharbi S.; Attiah A.; Alghazzawi D. | Integrating Blockchain With Artificial Intelligence To Secure Iot Networks: Future Trends | Sustainability (Switzerland), 14, 23 (2022) |
| 47699 | 0.887 | Thamaraiselvi K.; Pushpalatha A.; Chidambarathanu K.; Wankhede J.P.; Alagumuthukrishnan S.; Sarveshwaran V. | Securechainai: Integrating Blockchain And Artificial Intelligence For Enhanced Security In Iot Environments | Proceedings of the 5th International Conference on Smart Electronics and Communication, ICOSEC 2024 (2024) |
| 17680 | 0.887 | Islam R.; Bose R.; Roy S.; Khan A.A.; Sutradhar S.; Das S.; Ali F.; AlZubi A.A. | Decentralized Trust Framework For Smart Cities: A Blockchain-Enabled Cybersecurity And Data Integrity Model | Scientific Reports, 15, 1 (2025) |
| 921 | 0.887 | Padma A.; Ramaiah M.; Ravi V. | A Comprehensive Review Of Lightweight Blockchain Practices For Smart Cities: A Security And Efficacy Assessment | Journal of Reliable Intelligent Environments, 11, 3 (2025) |
| 53220 | 0.878 | Ayub K.; Alshawa R. | Strengthening Iot Security In Smart Cities: Servicenow Vulnerability Response Framework With Blockchain And Ai | Proceedings - ELTICOM 2024: 8th International Conference on Electrical, Telecommunication and Computer Engineering: Tech-Driven Innovations for Global Organizational Resilience (2024) |
| 36064 | 0.878 | Alfahaid A.; Alalwany E.; Almars A.M.; Alharbi F.; Atlam E.; Mahgoub I. | Machine Learning-Based Security Solutions For Iot Networks: A Comprehensive Survey | Sensors, 25, 11 (2025) |