2023
DOI: 10.1109/access.2023.3323891
|View full text |Cite
|
Sign up to set email alerts
|

A Deep Learning-Based IDS for Automotive Theft Detection for In-Vehicle CAN Bus

Junaid Ahmad Khan,
Dae-Woon Lim,
Young-Sik Kim

Abstract: Driver behavior features extracted from the controller area network (CAN) have potential applications in improving vehicle safety. However, the development of a classifier-based IDS for in-vehicle networks remains an open research problem. To address this challenge, we incorporate novel n-fold crossvalidation windowing techniques on two publicly available driving behavior datasets. A driver classificationbased IDS is proposed using the LSTM-FCN model that utilizes the strengths of both fully convolutional netw… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
references
References 69 publications
0
0
0
Order By: Relevance