On the application of sensor authentication with intrinsic physical features to vehicle security. (May 2021)

• Record Type: Journal Article
• Authors: Baldini, Gianmarco; Giuliani, Raimondo; Gemo, Monica; Dimc, Franc
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Signal processing -- Authentication -- Security -- Sensors -- Machine learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107053
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Cybersecurity in the automotive sector is becoming increasingly important as modern vehicles can be exposed to cybersecurity threats and regulatory fraud. Sensor identification and authentication is an important function in the vehicle life cycle. This paper proposes an alternative or complementary identification and authentication approach to cryptographic techniques based on the physical properties of the Hall sensor, which is commonly used in odometers and motion sensors. The application of different signal processing algorithms to an experimental dataset of 12 Hall sensors was evaluated. In particular, a dimensionality reduction with decimation filters was applied to improve the time efficiency of the identification and authentication process while achieving high identification accuracy (accuracy=95%) and high authentication accuracy. In addition, the results show that different transformations with different hyperparameters are able to generate a wide range of challenges and responses to support the authentication process.