# An AI Architecture with the Capability to Classify and Explain Hardware Trojans

arXiv: https://arxiv.org/abs/2407.04551 · HTML: https://mlsanity.com/paper/2407.04551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [Explainable Neural Networks based on Additive Index Models](https://mlsanity.com/paper/1806.01933/) (2018)
- [An AI Architecture with the Capability to Explain Recognition Results](https://mlsanity.com/paper/2406.08740/) (2024)

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

- [Explainability Methods for Hardware Trojan Detection: A Systematic Comparison](https://mlsanity.com/paper/2601.18696/) (2026)

## Abstract (arXiv)

Hardware trojan detection methods, based on machine learning (ML) techniques, mainly identify suspected circuits but lack the ability to explain how the decision was arrived at. An explainable methodology and architecture is introduced based on the existing hardware trojan detection features. Results are provided for explaining digital hardware trojans within a netlist using trust-hub trojan benchmarks.