Survey of Security Issues in Memristor-based Machine Learning Accelerators for RF Analysis
CoRR(2023)
摘要
We explore security aspects of a new computing paradigm that combines novel
memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to
construct a highly efficient analog and/or digital fabric that is especially
well-suited to Machine Learning (ML) inference processors for Radio Frequency
(RF) signals. Memristors have different properties than traditional CMOS which
can potentially be exploited by attackers. In addition, the mixed signal
approximate computing model has different vulnerabilities than traditional
digital implementations. However both the memristor and the ML computation can
be leveraged to create security mechanisms and countermeasures ranging from
lightweight cryptography, identifiers (e.g. Physically Unclonable Functions
(PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation
and leakage/attack detection methods. Three different threat models are
proposed: 1) Supply Chain, 2) Physical Attacks, and 3) Remote Attacks. For each
threat model, potential vulnerabilities and defenses are identified. This
survey reviews a variety of recent work from the hardware and ML security
literature and proposes open problems for both attack and defense. The survey
emphasizes the growing area of RF signal analysis and identification in terms
of the commercial space, as well as military applications and threat models. We
differ from other other recent surveys that target ML in general, neglecting RF
applications.
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