Detection of Malicious FPGA Bitstreams using CNN-Based Learning

Multi-tenant FPGAs are increasingly being used in cloud computing technologies. Users are able to access the FPGA fabric remotely to implement custom accelerators in the cloud. However, sharing FPGA resources by untrusted third-parties can lead to serious security threats. Attackers can configure a portion of the FPGA with a malicious bitstream. Such malicious use of the FPGA fabric may lead to severe voltage fluctuations and eventually crash the FPGA. Attackers can also use side-channel and fault attacks to extract secret information (e.g., secret key of an AES encryption module). We propose a convolutional neural network (CNN)-based defense mechanism to detect malicious circuits that are configured on an FPGA by learning features from the data-series representation of the bitstreams of malicious circuits. We use the classification accuracy, true-positive rate, and false-positive rate metrics to quantify the effectiveness of CNN-based classification of malicious bitstreams. Experimental results on Xilinx FPGAs demonstrate the effectiveness of the proposed method.