Hardware Trojan detection based on ELM neural network

Wang, Sixiang; Dong, Xiuze; Sun, Kewang; Cui, Qi; Li, Dongxu; He, Chu

doi:10.1109/cci.2016.7778952

Cited by 19 publications

(8 citation statements)

References 6 publications

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“…There exist a body of prior art solutions that use machine learning to remedy the impact of noise in the power/current profile of side-channel analysis [30] [31] [32]. Authors in [30] use the Extreme Learning Machine algorithm to detect HT by analyzing the power consumption and observing the resulting current portfolio as an input feature to their machine learning solution. The major limitation in this work is the size of detectable Trojans.…”

Section: Previous Work On Hw Trojan Detectionmentioning

confidence: 99%

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AVATAR: NN-Assisted Variation Aware Timing Analysis and Reporting for Hardware Trojan Detection

et al. 2021

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This paper presents AVATAR, a learning-assisted Trojan testing flow to detect hardware Trojans placed into fabricated ICs at an untrusted foundry, without needing a Golden IC. AVATAR is a sidechannel delay-based testing solution that is assisted by a learning model (process watchdog) for tracking the process drift and systematic process variation. AVATAR's process watchdog model is trained using a limited number of test samples, collected at test time, to tightly correlate the Static Timing Analysis results (generated at design time) to the test results (generated from clock frequency sweeping test). The experimental results confirm that AVATAR detects over 98% of (small) Trojans inserted in the selected benchmarks. We have complemented our proposed solution with a diagnostic test that 1) further reduces the false-positive rate of AVATAR Trojan detection to zero or near zero, and 2) pinpoints the net-location of the Trojan Trigger or Payload.

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Section: Previous Work On Hw Trojan Detectionmentioning

confidence: 99%

“…AVATAR provides several advantages compare to sidechannel power detection solutions including [30] [31] [32]. 1) It doesn't require access to a Golden IC for Trojan detection.…”

Section: Previous Work On Hw Trojan Detectionmentioning

confidence: 99%

AVATAR: NN-Assisted Variation Aware Timing Analysis and Reporting for Hardware Trojan Detection

et al. 2021

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“…BPNNs can adjust the network weights and thresholds during training to achieve a nonlinear mapping of the input and output as well as better generalization ability. -Extreme Learning Machine (ELM) [68] is a singlehidden-layer feed-forward ANN (SLFN) that can randomly initialize the input weight and bias and obtain the corresponding output weight. ELM has been widely used in many fields due to its high learning speed and good generalization ability.…”

Section: ) Supervised Learningmentioning

confidence: 99%

“…Some researchers have attempted to apply ANNs to sidechannel analysis. For example, Wang et al presented an HT detection method to indicate whether the ICs are Trojaninfected by sampling and classifying the current features using an ELM [68]. Li et al extracted the nonlinear features from the power-consumption through the HT detection model established by the BPNN [66], [67].…”

Section: -Reverse Engineeringmentioning

confidence: 99%

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A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

et al. 2020

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The remarkable success of machine learning (ML) in a variety of research domains has inspired academic and industrial communities to explore its potential to address hardware Trojan (HT) attacks. While numerous works have been published over the past decade, few survey papers, to the best of our knowledge, have systematically reviewed the achievements and analyzed the remaining challenges in this area. To fill this gap, this article surveys ML-based approaches against HT attacks available in the literature. In particular, we first provide a classification of all possible HT attacks and then review recent developments from four perspectives, i.e., HT detection, design-for-security (DFS), bus security, and secure architecture. Based on the review, we further discuss the lessons learned in and challenges arising from previous studies. Despite current work focusing more on chip-layer HT problems, it is notable that novel HT threats are constantly emerging and have evolved beyond chips and to the component, device, and even behavior layers, therein compromising the security and trustworthiness of the overall hardware ecosystem. Therefore, we divide the HT threats into four layers and propose a hardware Trojan defense (HTD) reference model from the perspective of the overall hardware ecosystem, therein categorizing the security threats and requirements in each layer to provide a guideline for future research in this direction.

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A Review Paper on Machine Learning Based Trojan Detection in the IoT Chips

Lavanya

Rajalakshmi

2022

Internet of Things and Connected Technologies

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Hardware Trojan detection based on ELM neural network

Cited by 19 publications

References 6 publications

AVATAR: NN-Assisted Variation Aware Timing Analysis and Reporting for Hardware Trojan Detection

AVATAR: NN-Assisted Variation Aware Timing Analysis and Reporting for Hardware Trojan Detection

A Survey on Machine Learning Against Hardware Trojan Attacks: Recent Advances and Challenges

A Review Paper on Machine Learning Based Trojan Detection in the IoT Chips

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