Automotive Cybersecurity Vulnerability Assessment Using the Common Vulnerability Scoring System and Bayesian Network Model

Wang, Yinghui; Yu, Bin; Yu, Haiyang; Xiao, Lingyun; Ji, Haojie; Zhao, Yanan

doi:10.1109/jsyst.2022.3230097

Cited by 6 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Contrary to this, the intelligent transportation system (ITS) incorporates vehicle-to-everything (V2X), which includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies to broaden the communication spectrum [5]. Furthermore, the enhanced hardware complexity and advanced integrated functionality have introduced a plethora of security vulnerabilities and attack scenarios for the automotive sector [6], [7]. Among the current security concerns in the automotive industry, vehicle theft is a serious problem.…”

Section: Introductionmentioning

confidence: 99%

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

Khan,

Lim,

Kim

2023

IEEE Access

View full text Add to dashboard Cite

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 network (FCN) and long short-term memory (LSTM) networks. These modules allow the model to learn spatial and temporal features and utilize contextual information. In addition, we combine three squeeze and excite (SnE) layers following FCN layers to incorporate adjacent spatial locations and augment a scaled dot product attention mechanism into the LSTM to improve its feature selection and extraction capabilities. Our proposed IDS uses hacking and countermeasure research lab (HCRL) and test datasets, which achieve an improvement in accuracy of 4.18% and 13.99% respectively, from the baseline LSTM-FCN model. The experimental results of our method exhibited an overall accuracy of 99.36% and 96.36% for both datasets and outperformed various state-of-the-art methods.

show abstract

Section: Introductionmentioning

confidence: 99%