# SAND: A Self-supervised and Adaptive NAS-Driven Framework for Hardware Trojan Detection

arXiv: https://arxiv.org/abs/2510.23643 · HTML: https://mlsanity.com/paper/2510.23643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning](https://mlsanity.com/paper/2009.07111/) (2020)
- [Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search](https://mlsanity.com/paper/2201.09785/) (2022)
- [AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection](https://mlsanity.com/paper/2204.06117/) (2022)

## Built on by (most cited first, 1 of 1)

- [TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion](https://mlsanity.com/paper/2601.17178/) (2026)

## Abstract (arXiv)

The globalized semiconductor supply chain has made Hardware Trojans (HT) a significant security threat to embedded systems, necessitating the design of efficient and adaptable detection mechanisms. Despite promising machine learning-based HT detection techniques in the literature, they suffer from ad hoc feature selection and the lack of adaptivity, all of which hinder their effectiveness across diverse HT attacks. In this paper, we propose SAND, a selfsupervised and adaptive NAS-driven framework for efficient HT detection. Specifically, this paper makes three key contributions. (1) We leverage self-supervised learning (SSL) to enable automated feature extraction, eliminating the dependency on manually engineered features. (2) SAND integrates neural architecture search (NAS) to dynamically optimize the downstream classifier, allowing for seamless adaptation to unseen benchmarks with minimal fine-tuning. (3) Experimental results show that SAND achieves a significant improvement in detection accuracy (up to 18.3%) over state-of-the-art methods, exhibits high resilience against evasive Trojans, and demonstrates strong generalization.