Robust Localization of Key Fob Using Channel Impulse Response of Ultra Wide Band Sensors for Keyless Entry Systems
This addresses security vulnerabilities in keyless entry systems for automotive applications, but it is incremental as it builds on existing neural network approaches.
The paper tackles the problem of localizing a key fob for keyless entry systems using neural networks with UWB sensors, and the result is a proposed multi-head self-supervised architecture that improves performance by up to 67% against adversarial attacks compared to baseline methods.
Using neural networks for localization of key fob within and surrounding a car as a security feature for keyless entry is fast emerging. In this paper we study: 1) the performance of pre-computed features of neural networks based UWB (ultra wide band) localization classification forming the baseline of our experiments. 2) Investigate the inherent robustness of various neural networks; therefore, we include the study of robustness of the adversarial examples without any adversarial training in this work. 3) Propose a multi-head self-supervised neural network architecture which outperforms the baseline neural networks without any adversarial training. The model's performance improved by 67% at certain ranges of adversarial magnitude for fast gradient sign method and 37% each for basic iterative method and projected gradient descent method.