Disguising Topology and Side-Channel Information through Covert Gate- and ML-Enabled IP Camouflaging

2026-03-26

AI Summary

This work addresses the growing threat posed by advanced reverse engineering to semiconductor intellectual property (IP), against which conventional obfuscation techniques offer insufficient protection due to their vulnerability to structural analysis and side-channel attacks. To counter this, the paper introduces a novel “mimetic deception” strategy that, for the first time, structurally and visually disguises a functional IP as an unrelated one while integrating side-channel resistance mechanisms. By misleading adversaries into adopting incorrect power consumption models, the approach effectively nullifies differential power analysis. The proposed method synergistically combines IP camouflage, graph matching, and DNAS-NAND gate arrays, demonstrating robustness under both graph neural network (GNN)-based node classification and power analysis. Experimental results confirm that this multi-layered obfuscation scheme significantly disrupts reverse engineering toolchains and markedly enhances IP security against both structural identification and side-channel attacks.

Abstract

Semiconductor intellectual property (IP) theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques. Traditional ``cryptic'' IC camouflaging methods typically focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis. This paper explores ``mimetic deception,'' where a functional IP (F) is designed to structurally and visually masquerade as a completely different appearance IP (A). We provide a comprehensive evaluation of three deceptive methodologies: IP Camouflage, Graph Matching, and DNAS-NAND Gate Array, analyzing their resilience against GNN-based node classification, and Differential Power Analysis (DPA). Crucially, we demonstrate that mimetic deception achieves a novel anti-side-channel defense: by forcing the mis-classification of cryptographic primitives, the adversary is led to apply an incorrect power model, causing the DPA attack to fail. Our results validate that this multi-layered approach effectively thwarts the entire RE toolchain by poisoning the structural and logical data used for netlist understanding.

Problem

• IP theft
• reverse engineering
• side-channel attack
• IC camouflaging
• structural analysis

Innovation

• mimetic deception
• IP camouflaging
• side-channel defense
• graph neural networks
• differential power analysis