Smart City Gnosys
Smart city article details
| Title | Intruder Detection In Vanet Data Streams Using Federated Learning For Smart City Environments |
|---|---|
| ID_Doc | 33323 |
| Authors | Arya M.; Sastry H.; Dewangan B.K.; Rahmani M.K.I.; Bhatia S.; Muzaffar A.W.; Bivi M.A. |
| Year | 2023 |
| Published | Electronics (Switzerland), 12, 4 |
| DOI | http://dx.doi.org/10.3390/electronics12040894 |
| Abstract | Vehicular networks improve quality of life, security, and safety, making them crucial to smart city development. With the rapid advancement of intelligent vehicles, the confidentiality and security concerns surrounding vehicular ad hoc networks (VANETs) have garnered considerable attention. VANETs are intrinsically more vulnerable to attacks than wired networks due to high mobility, common network medium, and lack of centrally managed security services. Intrusion detection (ID) servers are the first protection layer against cyberattacks in this digital age. The most frequently used mechanism in a VANET is intrusion detection systems (IDSs), which rely on vehicle collaboration to identify attackers. Regrettably, existing cooperative IDSs get corrupted and cause the IDSs to operate abnormally. This article presents an approach to intrusion detection based on the distributed federated learning (FL) of heterogeneous neural networks for smart cities. It saves time and resources by using the most efficient intruder detection approach. First, vehicles use a federated learning technique to develop local, deep learning-based IDS classifiers for VANET data streams. They then share their locally learned classifiers upon request, significantly reducing communication overhead with neighboring vehicles. Then, an ensemble of federated heterogeneous neural networks is constructed for each vehicle, including locally and remotely trained classifiers. Finally, the global ensemble model is again shared with local devices for their updating. The effectiveness of the suggested method for intrusion detection in VANETs is evaluated using performance indicators such as attack detection rates, classification accuracy, precision, recall, and F1 scores over a ToN-IoT data stream. The ID model shows 0.994 training and 0.981 testing accuracy. © 2023 by the authors. |
| Author Keywords | classification; data streams; deep learning; federated learning; intrusion detection system; machine learning; smart city; VANETs |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
2469  | 0.906 | Rabah N.B.; Idoudi H. | A Machine Learning Framework For Intrusion Detection In Vanet Communications | Emerging Trends in Cybersecurity Applications (2022) |
| 44548 | 0.905 | Bangui H.; Buhnova B. | Recent Advances In Machine-Learning Driven Intrusion Detection In Transportation: Survey | Procedia Computer Science, 184 (2021) |
| 47747 | 0.904 | Shivaanivarsha N.; Swetha J.; Lashmi V.L.R.; Yaswanth Rao G.S. | Securing Intelligent Vehicular Networks Using Ai-Driven Federated Learning | 2025 International Conference on Computing and Communication Technologies, ICCCT 2025 (2025) |
| 7023 | 0.903 | El-Shafai W.; Azar A.T.; Ahmed S. | Ai-Driven Ensemble Classifier For Jamming Attack Detection In Vanets To Enhance Security In Smart Cities | IEEE Access, 13 (2025) |
| 36054 | 0.898 | Masmoudi O.; Idoudi H.; Mosbah M. | Machine Learning-Based Intrusion Detection For Position Falsification Attack In The Internet Of Vehicles | Intelligent Systems for IoE Based Smart Cities (2023) |
| 2508 | 0.896 | Mishra D.; Moudgi S.; Virmani D.; Faniband Y.P.; Nandyal A.B.; Sahu P.K.; Singh G. | A Mathematical Framework For Enhancing Iot Security In Vanets: Optimizing Intrusion Detection Systems Through Machine Learning Algorithms | Communications on Applied Nonlinear Analysis, 31, 8s (2024) |
| 14729 | 0.892 | Ullah F.; Srivastava G.; Mostarda L.; Cacciagrano D. | Collaborative Intrusion Detection System For Intermittent 10 Vs Using Federated Learning And Deep Swarm Particle Optimization | 2024 IEEE 11th International Conference on Data Science and Advanced Analytics, DSAA 2024 (2024) |
| 8516 | 0.892 | Sousa B.; Magaia N.; Silva S. | An Intelligent Intrusion Detection System For 5G-Enabled Internet Of Vehicles | Electronics (Switzerland), 12, 8 (2023) |
| 36079 | 0.891 | Khatua S.; Das S.; Nandi A.; Kumar A.; De D.; Roy S. | Machine Learning-Inspired Intrusion Detection On Vehicular Communication For Route Planning | Lecture Notes in Networks and Systems, 1158 LNNS (2025) |
| 7302 | 0.888 | Alladi T.; Agrawal A.; Gera B.; Chamola V.; Yu F.R. | Ambient Intelligence For Securing Intelligent Vehicular Networks: Edge-Enabled Intrusion And Anomaly Detection Strategies | IEEE Internet of Things Magazine, 6, 1 (2023) |