AVATAR: NN-Assisted Variation Aware Timing Analysis and Reporting for Hardware Trojan Detection

Abstract

This paper presents AVATAR, a learning-assisted Trojan testing flow to detect hardware Trojans placed into fabricated ICs at an untrusted foundry, without needing a Golden IC. AVATAR is a side-channel delay-based testing solution that is assisted by a learning model (process watchdog) for tracking the process drift and systematic process variation. AVATAR’s process watchdog model is trained using a limited number of test samples, collected at test time, to tightly correlate the Static Timing Analysis results (generated at design time) to the test results (generated from clock frequency sweeping test). The experimental results confirm that AVATAR detects over 98% of (small) Trojans inserted in the selected benchmarks. We have complemented our proposed solution with a diagnostic test that 1) further reduces the false-positive rate of AVATAR Trojan detection to zero or near zero, and 2) pinpoints the net-location of the Trojan Trigger or Payload.

Keywords

• Hardware trojan
• clock frequency sweeping test
• neural network
• side channel analysis
• process variation
• process drift