Security and robustness of AI-driven IOTs with differential sensing schemes

Abstract

Unexpected faulty sensors can seriously compromise the mission of strategic assets driven by artificial intelligence (AI), autonomous unmanned vehicles, and robots. In life threatening operation, these devices must make real time decisions based on the vital information collected from the sensors. AI is vulnerable to sensor failures caused both by malicious actions and by system breakdown. We propose a new scheme that is providing, in real-time, a unique fingerprint of a pair of differential sensors operating as a Physically Unclonable Function (PUF). The solution can be implemented with little computational complexity or power usage on most IOT devices driven by AI and a set of sensors. The differential circuitry ensures the authenticity of the source of the information generated from the sensors, thereby preventing malicious actors from deliberately falsifying sensor data. Without the fingerprint from the sensors, these actors will have reduced opportunities to access the server thus limiting this attack vector. Faulty sensors are identifiable since their erratic fingerprints differ from those identified at launch. The data originating from bad sensors is thereby ignored in favor of the data generated by redundant sensors when available in the system. This technology also ensures that the accuracy of the information from the sensor is within normal tolerances for a real-time AI environment. The fingerprints are used to create unique encryption keys for standardized post quantum cryptographic based communication. We are hereby demonstrating experimentally the relevance of the proposed differential circuits for these targeted applications.