Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Author:

Yaqoob Shumayla1ORCID,Hussain Asad2ORCID,Subhan Fazli2ORCID,Pappalardo Giuseppina3ORCID,Awais Muhammad4

Affiliation:

1. Department of Electrical Electronic Computer and Telecommunication Engineering, University of Catania, Catania, Italy

2. Faculty of Engineering and Computer Science, National University of Modern Languages, Islamabad, Pakistan

3. Department of Civil Engineering and Architecture, University of Catania, Catania, Italy

4. Department of Computer Science, Edge Hill University Lancashire, Ormskirk, U.K.

Funder

SAFE DEMON SAFE Driving by E-Health Monitoring, Sicilian Region, Italy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing Network Security Through Granular Computing: A Clustering-by-Time Approach to NetFlow Traffic Analysis;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30

2. Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium;European Transport Research Review;2024-07-01

3. DMMQBIN: Design of an Efficient Triple Metaheuristic Model for QoS-Aware Blockchain deployments in IoV Networks;2024 IEEE International Conference on Contemporary Computing and Communications (InC4);2024-03-15

4. A Comprehensive Review of AI Techniques for Resource Management in Fog Computing: Trends, Challenges, and Future Directions;IEEE Access;2024

5. Analyzing Autoencoder-Based Intrusion Detection System Performance;Journal of Information Security and Cybercrimes Research;2023-12-17

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