Detection of counterfeit ICs using public identification sequences

Samarin, Peter; Lemke‐Rust, Kerstin

doi:10.1109/hst.2017.7951827

Cited by 3 publications

(1 citation statement)

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“…While there are several recent research in the area of counterfeit IC detection (details given in Section 3), most of the existing techniques suffer from following major limitations: (i) requires hardware modification in the design phase; (ii) requires extensive chip registration process and maintenance of large database; (iii) produces a large number of false positive results; (iv) time consuming and manual labor extensive etc. In general, detection of recycled Flash memories must follow the following criteria [4,5,13]:…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Detection Method for Recycled Flash Memory Using Timing Characteristics †

Sakib¹,

Kumari²,

Talukder³

et al. 2018

Cryptography

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Counterfeiting electronic components is a serious problem for the security and reliability of any electronic systems. Unfortunately, the number of counterfeit components has increased considerably after the introduction of horizontal semiconductor supply chain. In this paper, we propose and experimentally demonstrate an approach for detecting recycled Flash memory. The proposed method is based on measurement of change in Flash array characteristics (such as erase time, program time, fail bit count, etc.) with its usage. We find that erase time is the best metric to distinguish a used Flash chip from a fresh one for the following reasons: (1) erase time shows minimal variation among different fresh memory blocks/chip and (2) erase time increases significantly with usage. We verify our method for a wide range of commercial off the shelf Flash chips from several vendors, technology nodes, storage density and storage type (single-bit per cell and multi-bit per cell). The minimum detectable chip usage varies from 0.05% to 3.0% of its total lifetime depending on the exact details of the chip.

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Section: Introductionmentioning

confidence: 99%

Non-Invasive Detection Method for Recycled Flash Memory Using Timing Characteristics †

Sakib¹,

Kumari²,

Talukder³

et al. 2018

Cryptography

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Independent detection of recycled flash memory: Challenges and solutions

Kumari

Talukder

Sakib

et al. 2018

2018 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

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Machine Learning Vulnerability Analysis of FPGA-based Ring Oscillator PUFs and Counter Measures

Hazari

Oun

Niamat

2021

J. Emerg. Technol. Comput. Syst.

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Physical Unclonable Functions (PUFs) exploit the manufacturing process variations inherent in silicon-based chips to generate unique secret keys. Although PUFs are supposed to be unclonable or unbreakable, researchers have found that they are vulnerable to machine learning (ML) attacks. In this article, we analyze the vulnerability of different FPGA-based Ring Oscillator PUFs (ROPUFs) to machine learning attacks. The challenge-response pairs (CRPs) data obtained from different ROPUFs is trained using different machine learning algorithms. From the study, it is found that the Artificial Neural Network (ANN) models can be used to train the ROPUFs with a training accuracy of 99.9% and a prediction accuracy of 62% when 5,000 CRPs are used for a challenge-response ROPUF. In this article, we assume a realistic situation where a small set of the CRP dataset (approximately 15% maximum) is unscrupulously obtained by the hacker. A prediction accuracy of 62% makes the PUF vulnerable to machine learning attacks. Therefore, a secondary goal of this article is the design of a ROPUF capable of thwarting machine learning modeling attacks. The modified XOR-inverter ROPUF drastically reduces the prediction accuracy from 62% to 13.1%, thus making it increasingly difficult for hackers to attack the ROPUF.

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Detection of counterfeit ICs using public identification sequences

Cited by 3 publications

References 0 publications

Non-Invasive Detection Method for Recycled Flash Memory Using Timing Characteristics †

Non-Invasive Detection Method for Recycled Flash Memory Using Timing Characteristics †

Independent detection of recycled flash memory: Challenges and solutions

Machine Learning Vulnerability Analysis of FPGA-based Ring Oscillator PUFs and Counter Measures

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