Enhanced Visible Light Localization Based on Machine Learning and Optimized Fingerprinting in Wireless Sensor Networks

Cappelli, Irene; Carli, Federico; Fort, Ada; Intravaia, Matteo; Micheletti, Federico; Peruzzi, Giacomo; Vignoli, Valerio

doi:10.1109/tim.2023.3240220

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…The algorithm measures the voltage at two points in the CAN bus and uses the difference between the two measurements to identify the ECU. The algorithm can detect any masquerading attacks on the CAN bus [71][72][73][74][75][76].…”

Section: The State-of-the-art On Can Cybersecuritymentioning

confidence: 99%

Design and Experimental Assessment of Real-Time Anomaly Detection Techniques for Automotive Cybersecurity

Dini,

Saponara

2023

Sensors

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In recent decades, an exponential surge in technological advancements has significantly transformed various aspects of daily life. The proliferation of indispensable objects such as smartphones and computers underscores the pervasive influence of technology. This trend extends to the domains of the healthcare, automotive, and industrial sectors, with the emergence of remote-operating capabilities and self-learning models. Notably, the automotive industry has integrated numerous remote access points like Wi-Fi, USB, Bluetooth, 4G/5G, and OBD-II interfaces into vehicles, amplifying the exposure of the Controller Area Network (CAN) bus to external threats. With a recognition of the susceptibility of the CAN bus to external attacks, there is an urgent need to develop robust security systems that are capable of detecting potential intrusions and malfunctions. This study aims to leverage fingerprinting techniques and neural networks on cost-effective embedded systems to construct an anomaly detection system for identifying abnormal behavior in the CAN bus. The research is structured into three parts, encompassing the application of fingerprinting techniques for data acquisition and neural network training, the design of an anomaly detection algorithm based on neural network results, and the simulation of typical CAN attack scenarios. Additionally, a thermal test was conducted to evaluate the algorithm’s resilience under varying temperatures.

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