Security and Interpretability in Automotive Systems

TL;DR

This paper presents methods to enhance security in automotive CAN systems through power-based sender authentication and improves interpretability of machine learning models for better decision-making transparency.

Contribution

It introduces a power consumption-based sender authentication technique and explores explanation methods for black-box models in automotive security.

Findings

High accuracy of ECU sender identification in real-world tests

Effective explanation of model inputs with sensitivity analysis and generative reconstruction

Applicable to other safety-critical systems for transparency and security

Abstract

The lack of any sender authentication mechanism in place makes CAN (Controller Area Network) vulnerable to security threats. For instance, an attacker can impersonate an ECU (Electronic Control Unit) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address the insecure nature of the system, this thesis demonstrates a sender authentication technique that uses power consumption measurements of the electronic control units (ECUs) and a classification model to determine the transmitting states of the ECUs. The method's evaluation in real-world settings shows that the technique applies in a broad range of operating conditions and achieves good accuracy. A key challenge of machine learning-based security controls is the potential of false positives. A false-positive alert may induce panic in operators, lead to incorrect reactions, and in the long run cause alarm fatigue. For reliable decision-making in such a circumstance, knowing the cause for unusual model behavior is essential. But, the black-box nature of these models makes them uninterpretable. Therefore, another contribution of this thesis explores explanation techniques for inputs of type image and time series that (1) assign weights to individual inputs based on their sensitivity toward the target class, (2) and quantify the variations in the explanation by reconstructing the sensitive regions of the inputs using a generative model. In summary, this thesis (https://uwspace.uwaterloo.ca/handle/10012/18134) presents methods for addressing the security and interpretability in automotive systems, which can also be applied in other settings where safe, transparent, and reliable decision-making is crucial.