The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection

Core Concepts

This work focuses on advancing security research in the hardware design space by formally defining the realistic problem of Hardware Trojan (HT) detection as "The Seeker’s Dilemma." The authors aim to create a benchmark that allows for a more accurate evaluation of HT detection methods.

Abstract

"The Seeker's Dilemma" introduces a new approach to HT detection, focusing on creating realistic benchmarks with hidden HTs. The study evaluates existing HT detection tools and highlights the challenges faced in accurately detecting HT-infected circuits. By using Principal Component Analysis, the authors analyze the performance of different detection strategies and emphasize the need for diversified datasets to train better HT detectors.

Stats

We use our developed benchmark to evaluate three state-of-the-art HT detection tools. The dataset is then used to train a binary classifier, which reports all four classification cases. The PCA algorithm identifies principal components to decrease data dimensionality. Each circuit has 18 variations of its original HT-free version.

Quotes

Key Insights Distilled From

The Seeker's Dilemma

by Amin Sarihi,... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17918.pdf

Deeper Inquiries

How can diverse approaches be generated to broaden Hardware Trojan (HT) detection?

To generate diverse approaches for broadening HT detection, researchers can explore various insertion criteria and strategies used by attackers. By understanding the different ways in which HTs can be inserted into circuits, detectors can be trained to recognize a wider range of potential threats. Additionally, creating benchmarks with a mix of HT-infected and HT-free instances using different insertion techniques can help in training detectors to identify various types of malicious modifications. Collaboration among research groups worldwide to create and share their own benchmarks with hidden HTs will also contribute to diversifying the dataset used for training detectors.

What are the implications of biased training data on Machine Learning-based HT detectors?

Biased training data can have significant implications on Machine Learning-based HT detectors. When the training data is imbalanced or skewed towards one class (e.g., more instances labeled as either infected or clean), it may lead to a detector that is inclined towards favoring that particular class during classification. This bias could result in higher false positive or false negative rates, impacting the overall accuracy and reliability of the detector. It may also limit the detector's ability to generalize well on unseen data or new scenarios where there is a different distribution of classes.

How can functional restructuring techniques impact the accuracy of HT detection methods?

Functional restructuring techniques play a crucial role in impacting the accuracy of HT detection methods. These techniques alter the structure but maintain functionality, making it challenging for detectors to differentiate between original circuits and those with hidden Trojans. The transformation introduced by these techniques changes how signals propagate through circuits without changing their intended behavior, making it harder for traditional detection algorithms relying solely on structural analysis. Additionally, functional restructuring introduces variability in circuit layouts that might not align with known patterns from existing datasets used for training ML-based detectors like HW2VEC. This variation poses challenges for detecting hidden Trojans accurately since they might blend in with functionally transformed circuits, leading to misclassifications and reduced overall performance of detection tools trained on standard datasets without such transformations.