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In recent decades, due to the emerging requirements of computationacceleration, cloud FPGAs have become popular in public clouds. Major cloudservice providers, e.g. AWS and Microsoft Azure have provided FPGA computingresources in their infrastructure and have enabled users to design and deploytheir own accelerators on these FPGAs. Multi-tenancy FPGAs, where multipleusers can share the same FPGA fabric with certain types of isolation to improveresource efficiency, have already been proved feasible. However, this alsoraises security concerns. Various types of side-channel attacks targetingmulti-tenancy FPGAs have been proposed and validated. The awareness of securityvulnerabilities in the cloud has motivated cloud providers to take action toenhance the security of their cloud environments.  In FPGA security research papers, researchers always perform attacks underthe assumption that attackers successfully co-locate with victims and are awareof the existence of victims on the same FPGA board. However, the way to reachthis point, i.e., how attackers secretly obtain information regardingaccelerators on the same fabric, is constantly ignored despite the fact that itis non-trivial and important for attackers. In this paper, we present a novelfingerprinting attack to gain the types of co-located FPGA accelerators. Weutilize a seemingly non-malicious benchmark accelerator to sniff thecommunication link and collect performance traces of the FPGA-hostcommunication link. By analyzing these traces, we are able to achieve highclassification accuracy for fingerprinting co-located accelerators, whichproves that attackers can use our method to perform cloud FPGA acceleratorfingerprinting with a high success rate. As far as we know, this is the firstpaper targeting multi-tenant FPGA accelerator fingerprinting with thecommunication side-channel.

Gotcha! I Know What You are Doing on the FPGA Cloud: Fingerprinting Co-Located Cloud FPGA Accelerators via Measuring Communication Links