Smart City Gnosys
Smart city article details
| Title | Securing Smart Cities Through Machine Learning: A Honeypot-Driven Approach To Attack Detection In Internet Of Things Ecosystems |
|---|---|
| ID_Doc | 47782 |
| Authors | Ahmed Y.; Beyioku K.; Yousefi M. |
| Year | 2024 |
| Published | IET Smart Cities, 6, 3 |
| DOI | http://dx.doi.org/10.1049/smc2.12084 |
| Abstract | The rapid increase and adoption of Internet of Things (IoT) devices have introduced unprecedented conveniences into modern life. However, this growth has also ushered in a wave of cyberattacks targeting these often-vulnerable systems. Smart cities, relying on interconnected sensors, are particularly susceptible to attacks due to the expanded entry points created by these devices. A security breach in such systems can compromise personal data and disrupt entire ecosystems. Traditional security measures are inadequate against the evolving sophistication of cyberattacks. The authors aim to address these challenges by leveraging honeypot data and machine learning to enhance IoT security. The research focuses on three objectives: identifying datasets from IoT-targeted honeypots, evaluating machine learning algorithms for threat detection, and proposing comprehensive security solutions. Real-world cyber-attack datasets from diverse honeypots simulating IoT devices are analysed using various machine learning and neural network algorithms. Results demonstrate significant improvement in cyber-attack detection and mitigation when integrating honeypot data into IoT security frameworks. The authors advance knowledge and provides practical insights for implementing robust security measures in diverse IoT applications, filling a crucial research gap. © 2024 The Author(s). IET Smart Cities published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. |
| Author Keywords | artificial intelligence; computer network security; data analytics and machine learning; data structures; information security and privacy; IoT and mobile communications; networks and telematics; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
1448  | 0.94 | Muniswamy A.; Rathi R. | A Detailed Review On Enhancing The Security In Internet Of Things-Based Smart City Environment Using Machine Learning Algorithms | IEEE Access, 12 (2024) |
| 36064 | 0.928 | 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) |
| 58001 | 0.922 | Rangelov D.; Lämmel P.; Brunzel L.; Borgert S.; Darius P.; Tcholtchev N.; Boerger M. | Towards An Integrated Methodology And Toolchain For Machine Learning-Based Intrusion Detection In Urban Iot Networks And Platforms | Future Internet, 15, 3 (2023) |
| 6993 | 0.919 | Alhamdi M.J.M.; Lopez-Guede J.M.; AlQaryouti J.; Rahebi J.; Zulueta E.; Fernandez-Gamiz U. | Ai-Based Malware Detection In Iot Networks Within Smart Cities: A Survey | Computer Communications, 233 (2025) |
| 36080 | 0.909 | Ahanger T.A.; Ullah I.; Algamdi S.A.; Tariq U. | Machine Learning-Inspired Intrusion Detection System For Iot: Security Issues And Future Challenges | Computers and Electrical Engineering, 123 (2025) |
| 814 | 0.904 | Basheer L.; Ranjana P. | A Comparative Study Of Various Intrusion Detections In Smart Cities Using Machine Learning | 2022 International Conference on IoT and Blockchain Technology, ICIBT 2022 (2022) |
| 32899 | 0.903 | Sarker I.H.; Khan A.I.; Abushark Y.B.; Alsolami F. | Internet Of Things (Iot) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions And Research Directions | Mobile Networks and Applications, 28, 1 (2023) |
| 55462 | 0.903 | Abdulla H.; Al-Raweshidy H.; Awad W. | The Era Of Internet Of Things: Towards Better Security Using Machine Learning | 2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023 (2023) |
| 19240 | 0.902 | Rangani H.; Chandrashekar K. | Detection And Prevention Of Cyber Threats In Smart Cities Using Machine Learning And Intrusion Detection Systems | 2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024 - Proceedings (2024) |
| 9628 | 0.902 | Priyadarshini I. | Anomaly Detection Of Iot Cyberattacks In Smart Cities Using Federated Learning And Split Learning | Big Data and Cognitive Computing, 8, 3 (2024) |