| Title | Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning |
|---|---|
| ID_Doc | 41042 |
| Authors | Priyadarshini, I |
| Title | Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning |
| Year | 2024 |
| Published | Big Data And Cognitive Computing, 8, 3 |
| Abstract | The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and functionality of urban systems. This research presents an innovative approach to identifying anomalies caused by IoT cyberattacks in smart cities. The proposed method harnesses federated and split learning and addresses the dual challenge of enhancing IoT network security while preserving data privacy. This study conducts extensive experiments using authentic datasets from smart cities. To compare the performance of classical machine learning algorithms and deep learning models for detecting anomalies, model effectiveness is assessed using precision, recall, F-1 score, accuracy, and training/deployment time. The findings demonstrate that federated learning and split learning have the potential to balance data privacy concerns with competitive performance, providing robust solutions for detecting IoT cyberattacks. This study contributes to the ongoing discussion about securing IoT deployments in urban settings. It lays the groundwork for scalable and privacy-conscious cybersecurity strategies. The results underscore the vital role of these techniques in fortifying smart cities and promoting the development of adaptable and resilient cybersecurity measures in the IoT era. |
| https://www.mdpi.com/2504-2289/8/3/21/pdf?version=1708618712 |
Similar Articles
| ID | Score | Article |
|---|---|---|
38272
|
Saba, T; Khan, AR; Sadad, T; Hong, SP Securing the IoT System of Smart City against Cyber Threats Using Deep Learning(2022) | |
| 36314
|
Rashid, MM; Kamruzzaman, J; Hassan, MM; Imam, T; Wibowo, S; Gordon, S; Fortino, G Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications(2022) | |
| 38565
|
Muniswamy, A; Rathi, R A Detailed Review on Enhancing the Security in Internet of Things-Based Smart City Environment Using Machine Learning Algorithms(2024) | |
| 42459
|
Chui, KT; Gupta, BB; Liu, JQ; Arya, V; Nedjah, N; Almomani, A; Chaurasia, P A Survey of Internet of Things and Cyber-Physical Systems: Standards, Algorithms, Applications, Security, Challenges, and Future Directions(2023)Information, 14, 7 | |
| 37333
|
Almasri, MM; Alajlan, AM A novel-cascaded ANFIS-based deep reinforcement learning for the detection of attack in cloud IoT-based smart city applications(2023)Concurrency And Computation-Practice & Experience, 35.0, 22 | |
| 36781
|
Alrashdi, I; Alqazzaz, A; Aloufi, E; Alharthi, R; Zohdy, M; Ming, H AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Leaming(2019) | |
| 44260
|
Sriram, S; Vinayakumar, R; Alazab, M; Soman, KP Network Flow based IoT Botnet Attack Detection using Deep Learning(2020) | |
| 41197
|
Girubagari, N; Ravi, TN Methods of anomaly detection for the prevention and detection of cyber attacks(2023)International Journal Of Intelligent Engineering Informatics, 11, 4 | |
| 41783
|
Prazeres, N; Costa, RLD; Santos, L; Rabadao, C Engineering the application of machine learning in an IDS based on IoT traffic flow(2023) | |
| 45810
|
Rashid, MM; Kamruzzaman, J; Hassan, MM; Imam, T; Gordon, S Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques(2020)International Journal Of Environmental Research And Public Health, 17, 24 |