research-article

Machine Learning-Based Intrusion Detection System for Controller Area Networks

Authors:

Abdulaziz Almehmadi,

Khalil El-KhatibAuthors Info & Claims

DIVANet '20: Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

Pages 41 - 47

https://doi.org/10.1145/3416014.3424581

Published: 16 November 2020 Publication History

Abstract

The automotive industry continues to innovate at an exponential rate to provide a safer and more efficient experience for consumers. Autonomous vehicles and Vehicle-to-Everything technologies are at the forefront of defining the future of transportation. Enabling vehicles to connect to various services has exposed critical in-vehicle networks such as the Controller Area Network (CAN) to potential exploitation by adversaries. In its standard form, the CAN bus suffers from multiple vulnerabilities such as limited bandwidth and lack of authentication. Attacks can be initiated through physical and wireless mediums, exploiting diagnostic interfaces, Bluetooth and infotainment systems to compromise the confidentiality, integrity and availability of data communication within vehicles. In this paper, a holistic, comprehensive, Machine Learning-Based intrusion detection system for the CAN bus is proposed to secure the critical in-vehicle network. The proposed system is modular, scalable and can be adapted to the ever-changing threat landscape of cyber vehicle attacks. On an unseen testing dataset, our system achieved 100% accuracy in protecting against denial of service and multiple impersonation injection attacks, as well as 95.67% accuracy of fuzzy injection attacks.

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Cited By

Kousar AAhmed SAltamimi AKhan Z(2024)A Novel Light-Weight Machine Learning Classifier for Intrusion Detection in Controller Area Network in Smart CarsSmart Cities10.3390/smartcities70601277:6(3289-3314)Online publication date: 2-Nov-2024
https://doi.org/10.3390/smartcities7060127
Tanksale V(2024)Intrusion detection system for controller area networkCybersecurity10.1186/s42400-023-00195-47:1Online publication date: 2-Feb-2024
https://doi.org/10.1186/s42400-023-00195-4
Jeong YKim HLee SChoi WLee DJo H(2024)In-Vehicle Network Intrusion Detection System Using CAN Frame-Aware FeaturesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332362225:5(3843-3853)Online publication date: May-2024
https://doi.org/10.1109/TITS.2023.3323622
Show More Cited By

Index Terms

Machine Learning-Based Intrusion Detection System for Controller Area Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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Published In

cover image ACM Conferences

DIVANet '20: Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications

November 2020

76 pages

ISBN:9781450381215

DOI:10.1145/3416014

General Chair:
Mirela Notare
Technology University on Fly Transportation, Brazil
,
Program Chair:
Peng Sun
Duke Kunshan University, China

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

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MSWiM '20

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SIGSIM

MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 16 - 20, 2020

Alicante, Spain

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Overall Acceptance Rate 70 of 308 submissions, 23%

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Citations

Cited By

Kousar AAhmed SAltamimi AKhan Z(2024)A Novel Light-Weight Machine Learning Classifier for Intrusion Detection in Controller Area Network in Smart CarsSmart Cities10.3390/smartcities70601277:6(3289-3314)Online publication date: 2-Nov-2024
https://doi.org/10.3390/smartcities7060127
Tanksale V(2024)Intrusion detection system for controller area networkCybersecurity10.1186/s42400-023-00195-47:1Online publication date: 2-Feb-2024
https://doi.org/10.1186/s42400-023-00195-4
Jeong YKim HLee SChoi WLee DJo H(2024)In-Vehicle Network Intrusion Detection System Using CAN Frame-Aware FeaturesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332362225:5(3843-3853)Online publication date: May-2024
https://doi.org/10.1109/TITS.2023.3323622
Yin YMerchan JPappachan PSekar V(2024)Work-in-Progress: CANGen: Practical Synthetic CAN Traces Generation Using Deep Generative Models2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)10.1109/EuroSPW61312.2024.00050(1-9)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSPW61312.2024.00050
M SR S P(2024)RETRACTED ARTICLE: An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networksScientific Reports10.1038/s41598-024-70835-314:1Online publication date: 5-Sep-2024
https://doi.org/10.1038/s41598-024-70835-3
Raio SCorder KParker TShearer GEdwards JThogaripally MPark SNelson F(2023)Reinforcement Learning as a Path to Autonomous Intelligent Cyber-Defense Agents in Vehicle PlatformsApplied Sciences10.3390/app13211162113:21(11621)Online publication date: 24-Oct-2023
https://doi.org/10.3390/app132111621
Alsulami AAl-Haija QAlturki BAlqahtani AAlsini R(2023)Security strategy for autonomous vehicle cyber-physical systems using transfer learningJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00564-x12:1Online publication date: 20-Dec-2023
https://dl.acm.org/doi/10.1186/s13677-023-00564-x
Mazloomi FShah Heydari SEl-Khatib KCoutinho RBani Younes M(2023)Trust-based Knowledge Sharing Among Federated Learning Servers in Vehicular Edge ComputingProceedings of the Int'l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications10.1145/3616392.3624701(9-15)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1145/3616392.3624701
Rajapaksha SKalutarage HAl-Kadri MPetrovski AMadzudzo GCheah M(2023)AI-Based Intrusion Detection Systems for In-Vehicle Networks: A SurveyACM Computing Surveys10.1145/357095455:11(1-40)Online publication date: 9-Feb-2023
https://dl.acm.org/doi/10.1145/3570954
Wang LDu SGong GLei Y(2023)Design and Test of Hardware-in-the-Loop Platform for Vehicle Chassis CAN System with Intermittent Open and Short Connection Faults2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)10.1109/M2VIP58386.2023.10413404(1-6)Online publication date: 21-Nov-2023
https://doi.org/10.1109/M2VIP58386.2023.10413404
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