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

• URL: http://arxiv.org/abs/2402.17918v1
• Date: Tue, 27 Feb 2024 22:14:01 GMT
• Title: The Seeker's Dilemma: Realistic Formulation and Benchmarking for Hardware Trojan Detection
• Authors: Amin Sarihi, Ahmad Patooghy, Abdel-Hameed A. Badawy, Peter Jamieson
• Abstract summary: This work focuses on advancing security research in the hardware design space by formally defining the realistic problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker's Dilemma" We create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work focuses on advancing security research in the hardware design space by formally defining the realistic problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker's Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether circuits are infected by HTs or not. Using this theoretical problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected or not. We believe that our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our developed benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors.

Related papers

• Hiding in Plain Sight: Reframing Hardware Trojan Benchmarking as a Hide&Seek Modification [0.0]
  This work focuses on advancing security research in the hardware design space by formally defining the realistic problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as The Seeker's Dilemma where a detecting agent is unaware of whether circuits are infected by HTs or not.
  arXiv  Detail & Related papers  (2024-10-21T00:45:20Z)
• Evasive Hardware Trojan through Adversarial Power Trace [6.949268510101616]
  We introduce a HT obfuscation (HTO) approach to allow HTs to bypass detection method. HTO can be implemented with only a single transistor for ASICs and FPGAs. We show that an adaptive attacker can still design evasive HTOs by constraining the design with a spectral noise budget.
  arXiv  Detail & Related papers  (2024-01-04T16:28:15Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection [0.0]
  Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. We introduce the first automated Reinforcement Learning (RL) HT insertion and detection framework to address these shortcomings.
  arXiv  Detail & Related papers  (2023-05-16T16:42:07Z)
• Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images [55.09446477517365]
  Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. We propose a more Threatening PA without the scarification of the visual quality, dubbed TPA. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.
  arXiv  Detail & Related papers  (2023-02-13T02:35:49Z)
• ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning [6.87143729255904]
  We develop an automated, scalable, and practical attack framework, ATTRITION, using reinforcement learning (RL) ATTRITION evades eight detection techniques across two HT detection categories, showcasing its behavior. We demonstrate ATTRITION's ability to evade detection techniques by evaluating designs ranging from the widely-used academic suites to larger designs such as the open-source MIPS and mor1kx processors to AES and a GPS module.
  arXiv  Detail & Related papers  (2022-08-26T23:47:47Z)
• DETERRENT: Detecting Trojans using Reinforcement Learning [8.9149615294509]
  Hardware Trojans (HTs) are a pernicious threat to integrated circuits. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs.
  arXiv  Detail & Related papers  (2022-08-26T22:09:47Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Scalable Backdoor Detection in Neural Networks [61.39635364047679]
  Deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch. We propose a novel trigger reverse-engineering based approach whose computational complexity does not scale with the number of labels, and is based on a measure that is both interpretable and universal across different network and patch types. In experiments, we observe that our method achieves a perfect score in separating Trojaned models from pure models, which is an improvement over the current state-of-the art method.
  arXiv  Detail & Related papers  (2020-06-10T04:12:53Z)
• AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching [76.4844593082362]
  We investigate the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong baseline for remote HR measurement with architecture search (NAS) Comprehensive experiments are performed on three benchmark datasets on both intra-temporal and cross-dataset testing.
  arXiv  Detail & Related papers  (2020-04-26T05:43:21Z)
• Solving Missing-Annotation Object Detection with Background Recalibration Loss [49.42997894751021]
  This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets. Previous art has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
  arXiv  Detail & Related papers  (2020-02-12T23:11:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.