An AI-Enabled Side Channel Power Analysis Based Hardware Trojan Detection Method for Securing the Integrated Circuits in Cyber-Physical Systems

• URL: http://arxiv.org/abs/2411.12721v1
• Date: Tue, 19 Nov 2024 18:39:20 GMT
• Title: An AI-Enabled Side Channel Power Analysis Based Hardware Trojan Detection Method for Securing the Integrated Circuits in Cyber-Physical Systems
• Authors: Sefatun-Noor Puspa, Abyad Enan, Reek Majumdar, M Sabbir Salek, Gurcan Comert, Mashrur Chowdhury,
• Abstract summary: One of the stealthiest threats is the insertion of a hardware trojan into an IC. Trojans can severely compromise system safety and security. This paper presents a non-invasive method for hardware trojan detection based on side-channel power analysis.
• Score: 7.333490062088133
• License:
• Abstract: Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). ICs are largely susceptible to various hardware attacks with malicious intents. One of the stealthiest threats is the insertion of a hardware trojan into the IC, causing the circuit to malfunction or leak sensitive information. Due to supply chain vulnerabilities, ICs face risks of trojan insertion during various design and fabrication stages. These trojans typically remain inactive until triggered. Once triggered, trojans can severely compromise system safety and security. This paper presents a non-invasive method for hardware trojan detection based on side-channel power analysis. We utilize the dynamic power measurements for twelve hardware trojans from IEEE DataPort. Our approach applies to signal processing techniques to extract crucial time-domain and frequency-domain features from the power traces, which are then used for trojan detection leveraging Artificial Intelligence (AI) models. Comparison with a baseline detection approach indicates that our approach achieves higher detection accuracy than the baseline models used on the same side-channel power dataset.

Related papers

• SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection [3.048384587446267]
  Many IC companies outsource manufacturing to third-party foundries, creating security risks such as stealthy analog Trojans. Traditional detection methods, including embedding circuit watermarks or conducting hardware-based monitoring, often impose significant area and power overheads. We propose SPICED, a framework that operates within the software domain, eliminating the need for hardware modifications for Trojan detection and localization.
  arXiv  Detail & Related papers  (2024-08-25T17:07:08Z)
• TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans [21.7375312616769]
  TroLLoc is a novel scheme for IC security closure that employs, for the first time, logic locking and layout hardening in unison. We show that TroLLoc successfully renders layouts resilient, with reasonable overheads, against (i.e., general prospects for Trojan insertion as in the ISPD'22 contest, (ii) actual Trojan insertion as in the ISPD'23 contest, and (iii) potential second-order attacks.
  arXiv  Detail & Related papers  (2024-05-09T07:25:38Z)
• Towards Practical Fabrication Stage Attacks Using Interrupt-Resilient Hardware Trojans [4.549209593575401]
  We introduce a new class of hardware trojans called interrupt-resilient trojans (IRTs) IRTs can successfully address the problem of non-deterministic triggering in CPUs. We show that our design allows for seamless integration during fabrication stage attacks.
  arXiv  Detail & Related papers  (2024-03-15T19:55:23Z)
• TrojanNet: Detecting Trojans in Quantum Circuits using Machine Learning [5.444459446244819]
  TrojanNet is a novel approach to enhance the security of quantum circuits by detecting and classifying Trojan-inserted circuits. We generate 12 diverse datasets by introducing variations in Trojan gate types, the number of gates, insertion locations, and compilers. Experimental results showcase an average accuracy of 98.80% and an average F1-score of 98.53% in effectively detecting and classifying Trojan-inserted QAOA circuits.
  arXiv  Detail & Related papers  (2023-06-29T05:56:05Z)
• Evil from Within: Machine Learning Backdoors through Hardware Trojans [72.99519529521919]
  Backdoors pose a serious threat to machine learning, as they can compromise the integrity of security-critical systems, such as self-driving cars. We introduce a backdoor attack that completely resides within a common hardware accelerator for machine learning. We demonstrate the practical feasibility of our attack by implanting our hardware trojan into the Xilinx Vitis AI DPU.
  arXiv  Detail & Related papers  (2023-04-17T16:24:48Z)
• Security Closure of IC Layouts Against Hardware Trojans [18.509106432984094]
  We propose a multiplexer-based logic-locking scheme that is (i) devised for layout-level Trojan prevention, (ii) resilient against state-of-the-art, oracle-less machine learning attacks, and (iii) fully integrated into a tailored, yet generic, commercial-grade design flow. We show that ours can render layouts resilient, with reasonable overheads, against Trojan insertion in general and also against second-order attacks (i.e., adversaries seeking to bypass the locking defense in an oracle-less setting)
  arXiv  Detail & Related papers  (2022-11-15T09:17:49Z)
• Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free [126.15842954405929]
  Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a trigger. We propose a novel Trojan network detection regime: first locating a "winning Trojan lottery ticket" which preserves nearly full Trojan information yet only chance-level performance on clean inputs; then recovering the trigger embedded in this already isolated subnetwork.
  arXiv  Detail & Related papers  (2022-05-24T06:33:31Z)
• Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases [87.69818690239627]
  We study the problem of the Trojan network (TrojanNet) detection in the data-scarce regime. We propose a data-limited TrojanNet detector (TND), when only a few data samples are available for TrojanNet detection. In addition, we propose a data-free TND, which can detect a TrojanNet without accessing any data samples.
  arXiv  Detail & Related papers  (2020-07-31T02:00:38Z)
• Cassandra: Detecting Trojaned Networks from Adversarial Perturbations [92.43879594465422]
  In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan behaviors into the models. We propose a method to verify if a pre-trained model is Trojaned or benign. Our method captures fingerprints of neural networks in the form of adversarial perturbations learned from the network gradients.
  arXiv  Detail & Related papers  (2020-07-28T19:00:40Z)
• Odyssey: Creation, Analysis and Detection of Trojan Models [91.13959405645959]
  Trojan attacks interfere with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that contain the trigger. Existing Trojan detectors make strong assumptions about the types of triggers and attacks. We propose a detector that is based on the analysis of the intrinsic properties; that are affected due to the Trojaning process.
  arXiv  Detail & Related papers  (2020-07-16T06:55:00Z)
• An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks [59.42357806777537]
  trojan attack aims to attack deployed deep neural networks (DNNs) relying on hidden trigger patterns inserted by hackers. We propose a training-free attack approach which is different from previous work, in which trojaned behaviors are injected by retraining model on a poisoned dataset. The proposed TrojanNet has several nice properties including (1) it activates by tiny trigger patterns and keeps silent for other signals, (2) it is model-agnostic and could be injected into most DNNs, dramatically expanding its attack scenarios, and (3) the training-free mechanism saves massive training efforts compared to conventional trojan attack methods.
  arXiv  Detail & Related papers  (2020-06-15T04:58:28Z)

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.