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To improve the modeling resilience of silicon strong physical unclonablefunctions (PUFs), in particular, the APUFs, that yield a very large number ofchallenge response pairs (CRPs), a number of composited APUF variants such asXOR-APUF, interpose-PUF (iPUF), feed-forward APUF (FF-APUF),and OAX-APUF havebeen devised. When examining their security in terms of modeling resilience,utilizing multiple information sources such as power side channel information(SCI) or/and reliability SCI given a challenge is under-explored, which poses achallenge to their supposed modeling resilience in practice. Building uponmulti-label/head deep learning model architecture,this work proposesMulti-Label Multi-Side-channel-information enabled deep learning Attacks(MLMSA) to thoroughly evaluate the modeling resilience of aforementioned APUFvariants. Despite its simplicity, MLMSA can successfully break large-scaledAPUF variants, which has not previously been achieved. More precisely, theMLMSA breaks 128-stage 30-XOR-APUF, (9, 9)- and (2, 18)-iPUFs, and (2, 2,30)-OAX-APUF when CRPs, power SCI and reliability SCI are concurrently used. Itbreaks 128-stage 12-XOR-APUF and (2, 2, 9)-OAX-APUF even when only theeasy-to-obtain reliability SCI and CRPs are exploited. The 128-stage six-loopFF-APUF and one-loop 20-XOR-FF-APUF can be broken by simultaneously usingreliability SCI and CRPs. All these attacks are normally completed within anhour with a standard personalcomputer. Therefore, MLMSA is a useful techniquefor evaluating other existing or any emerging strong PUF designs.

MLMSA: Multi-Label Multi-Side-Channel-Information enabled Deep Learning Attacks on APUF Variants