Abstract
This thesis addresses the prevalence of electronic hardware level threats in today’s world, which range from counterfeit or malicious hardware in supply chains to electronic attack tools. These threats can be rapidly changing and highly customized, making validating hardware dicult. Measurement devices have been developed to identify these threats, but they require a high level of expertise to deploy.
This thesis addresses these challenges by proposing a novelty detection method for the SilGeo hardware validation platform with low deployment barriers. The method can be trained on as few as three valid devices, and the entire training and application process is fully automated, requiring no expertise. The method incorporates Bayesian statistical models that rely on carefully selected assumptions and domain knowledge. Furthermore, maximum false positive rates can be estimated and adjusted without additional data.
The proposed method is tested in several case studies on devices ranging from surfacemount integrated circuits to Wi-Fi-enabled disguised attack tools. In each case, the estimated maximum false positive rate exceeded the observed false positive rate, and most counterfeit and malicious devices were identified. This thesis presents a practical solution to detecting and validating hardware in a rapidly changing threat landscape.
Supervisor: Sebastian Fischmeister