Intelligent Tamper Detection Systems using Machine Learning

Abstract

Existing anti-tamper designs protect against limited forms of attacks and have deterministic tamper responses, which can undermine the availability of systems. Advancements in physical inspection techniques have enabled stealthier attacks. Therefore, there is a pressing need for more intelligent defenses that ensure a longer operational time while keeping up with the expected increase in the capabilities of adversaries. This study proposes to enhance existing physical protection methods by developing an intelligent anti-tamper using machine learning algorithms. It uses an analytic system capable of detecting and classifying multiple types of behaviors (e.g., normal operation conditions, known attack vectors, and anomalous behavior). A prototype of the proposed system has been implemented and its functionality has been successfully verified for two types of normal operating conditions and further four forms of physical attacks. In addition, a systematic threat modelling analysis and security validation was carried out, which indicated the proposed solution provides better protection against including information leakage, loss of data, and disruption of operation.