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: | , , , , , , |
|---|---|
| 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!
|
| _version_ | 1832582328471257088 |
|---|---|
| author | Mohammed Almehdhar Abdullatif Albaseer Muhammad Asif Khan Mohamed Abdallah Hamid Menouar Saif Al-Kuwari Ala Al-Fuqaha |
| author_facet | Mohammed Almehdhar Abdullatif Albaseer Muhammad Asif Khan Mohamed Abdallah Hamid Menouar Saif Al-Kuwari Ala Al-Fuqaha |
| author_sort | Mohammed Almehdhar |
| collection | DOAJ |
| description | 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 deep learning (DL) approaches employed in developing sophisticated IDS for safeguarding IVNs against potential cyber-attacks. Specifically, we focus on the Controller Area Network (CAN) protocol, which is prevalent in in-vehicle communication systems, yet exhibits inherent security vulnerabilities. We propose a novel taxonomy categorizing IDS techniques into conventional ML, DL, and hybrid models, highlighting their applicability in detecting and mitigating various cyber threats, including spoofing, eavesdropping, and denial-of-service attacks. We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. Additionally, we explore emerging technologies, such as Federated Learning (FL) and Transfer Learning, to enhance the robustness and adaptability of IDS solutions. Based on our thorough analysis, we identify key limitations in current methodologies and propose potential paths for future research, focusing on integrating real-time data analysis, cross-layer security measures, and collaborative IDS frameworks. |
| format | Article |
| id | doaj-art-aa5cac1ab5324e2b83a327ca77eac785 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-aa5cac1ab5324e2b83a327ca77eac7852025-01-30T00:04:30ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-01586990610.1109/OJVT.2024.342225310582439Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle NetworksMohammed Almehdhar0https://orcid.org/0009-0006-7012-6700Abdullatif Albaseer1https://orcid.org/0000-0002-6886-6500Muhammad Asif Khan2https://orcid.org/0000-0003-2925-8841Mohamed Abdallah3https://orcid.org/0000-0002-3261-7588Hamid Menouar4Saif Al-Kuwari5https://orcid.org/0000-0002-4402-7710Ala Al-Fuqaha6https://orcid.org/0000-0002-0903-1204Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarQatar Mobility Innovations Center, Qatar University, Doha, QatarDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarQatar Mobility Innovations Center, Qatar University, Doha, QatarDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDivision of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarThe 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 deep learning (DL) approaches employed in developing sophisticated IDS for safeguarding IVNs against potential cyber-attacks. Specifically, we focus on the Controller Area Network (CAN) protocol, which is prevalent in in-vehicle communication systems, yet exhibits inherent security vulnerabilities. We propose a novel taxonomy categorizing IDS techniques into conventional ML, DL, and hybrid models, highlighting their applicability in detecting and mitigating various cyber threats, including spoofing, eavesdropping, and denial-of-service attacks. We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. Additionally, we explore emerging technologies, such as Federated Learning (FL) and Transfer Learning, to enhance the robustness and adaptability of IDS solutions. Based on our thorough analysis, we identify key limitations in current methodologies and propose potential paths for future research, focusing on integrating real-time data analysis, cross-layer security measures, and collaborative IDS frameworks.https://ieeexplore.ieee.org/document/10582439/In-vehicle network (IVN)intrusion detection system (IDS)machine learning (ML)deep learning (DL)cybersecuritycontroller area network (CAN) |
| spellingShingle | Mohammed Almehdhar Abdullatif Albaseer Muhammad Asif Khan Mohamed Abdallah Hamid Menouar Saif Al-Kuwari Ala Al-Fuqaha Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks IEEE Open Journal of Vehicular Technology In-vehicle network (IVN) intrusion detection system (IDS) machine learning (ML) deep learning (DL) cybersecurity controller area network (CAN) |
| title | Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks |
| title_full | Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks |
| title_fullStr | Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks |
| title_full_unstemmed | Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks |
| title_short | Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks |
| title_sort | deep learning in the fast lane a survey on advanced intrusion detection systems for intelligent vehicle networks |
| topic | In-vehicle network (IVN) intrusion detection system (IDS) machine learning (ML) deep learning (DL) cybersecurity controller area network (CAN) |
| url | https://ieeexplore.ieee.org/document/10582439/ |
| work_keys_str_mv | AT mohammedalmehdhar deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks AT abdullatifalbaseer deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks AT muhammadasifkhan deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks AT mohamedabdallah deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks AT hamidmenouar deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks AT saifalkuwari deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks AT alaalfuqaha deeplearninginthefastlaneasurveyonadvancedintrusiondetectionsystemsforintelligentvehiclenetworks |