FFD: A Framework for Fake Flash Detection

  • Authors:
    Zimu Guo (Univ. of Florida), Xiaolin Xu (Univ. of Florida), Mark M. Tehranipoor (Univ. of Florida), Domenic Forte (Univ. of Florida)
    Publication ID:
    P090438
    Publication Type:
    Paper
    Received Date:
    28-Feb-2017
    Last Edit Date:
    28-Feb-2017
    Research:
    2648.001 (University of Florida)

Abstract

Counterfeit electronics have become a big concern in the globalized semiconductor industry where chips might be recycled, remarked, cloned or overproduced. In this work, we advance the state-of-the-art counterfeit detection of Flash memory, which is widely used in electronic systems. Fake memories may be used in critical systems, such as missiles, military aircrafts, and helicopters, thus diminishing their reliability. In addition, there are countless stories of fake Flash drives in the general consumer market. We propose a comprehensive framework called FFD to detect fake Flash memories (i.e., recycled, remarked and cloned parts).FFD is validated with 200,000 commercial Flash memory pages. Experimental results show that our framework performs well in: 1) nearly 100% detection accuracy of Flash with as little as 5% usage, 2) estimating the Flash memory usage with high resolution (greater than or equal to 5% of its maximal endurance). Another contribution of this work is a chip ID generation technique that can generate unique Flash fingerprints with greater than 99.3% reliability.

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