Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks
The rapid evolution of modern automobiles into intelligent and interconnected entities presents new challenges in cybersecurity, particularly in Intrusion Detection Systems (IDS) for In-Vehicle Networks (IVNs). This survey paper offers an in-depth examination of advanced machine learning (ML) and de...
Saved in:
Main Authors: | Mohammed Almehdhar, Abdullatif Albaseer, Muhammad Asif Khan, Mohamed Abdallah, Hamid Menouar, Saif Al-Kuwari, Ala Al-Fuqaha |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Open Journal of Vehicular Technology |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10582439/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches
by: Shehla Gul, et al.
Published: (2024-12-01) -
A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure
by: Sardar Muhammad Ali, et al.
Published: (2025-01-01) -
A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles
by: Pradeep Kumar Tiwari, et al.
Published: (2025-01-01) -
Deep Learning-Based Intrusion Detection System for Detecting IoT Botnet Attacks: A Review
by: Tamara Al-Shurbaji, et al.
Published: (2025-01-01) -
Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications
by: Mohammad Aldossary, et al.
Published: (2025-01-01)