Enhancing Automobile CAN bus Security with a Machine Learning Intrusion Detection System

As time goes on, manufacturers are increasingly adding electronic and technological features to their fleet. Consumers enjoy the additional features, but the additions come at the expense of increasing the automobiles’ cyber footprint. While manufacturers attempt to secure their vehicles, recent events have shown that the attempts have failed to thwart malicious actors from hacking into vehicles. This research created a machine learning intrusion detection system utilizing AI (artificial intelligence) to detect attacks on the CAN bus. The CAN bus allows electronic components on an automobile to communicate with each other, making it a prime target for attackers. The research examined how various algorithms performed in detecting various types of attacks against the CAN bus. The research discussed various ways an automobile can run the IDS and aims to determine whether an automobile can run the IDS system.

Author

• Belle, Cody N

Language

• en

Keyword

• automobile
• can bus
• artificial intelligence
• machine learning
• cybersecurity

Date created

• 2026

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Cybersecurity Analytics

Degree

• D.Engr.

Advisor

• Alexander, Max

Committee Member(s)

• Clements, Ken
• Fossaceca, John

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/c247dt02g