Detection and Classification of Malicious Bitstreams for FPGAs in Cloud Computing
Detection and Classification of Malicious Bitstreams for FPGAs in Cloud Computing
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云计算中 FPGA 恶意比特流的检测和分类
DOI：
10.1145/3566097.3568346
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Chakrabarty, Krishnendu
中科院分区：
文献类型：
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作者：
Chaudhuri, Jayeeta;Chakrabarty, Krishnendu
随着FPGA越来越多地被多个用户和第三方共享和远程访问，它们带来了重大的安全问题。在FPGA上运行的模块可以包括引起基于电压的故障攻击和拒绝服务（DoS）的电路。攻击者可能会使用实现恶意电路的位流配置FPGA的某些区域。攻击者还可以执行侧信道分析和故障攻击以提取秘密信息（例如，AES加密的密钥）。在本文中，我们提出了一种基于卷积神经网络（CNN）的防御检测基于RO的恶意电路的比特流通过分析从FPGA比特流中提取的静态特征。我们进一步探讨的关键性RO为基础的电路，以检测恶意木马配置在FPGA上。在Xilinx FPGA上的评估证明了安全解决方案的有效性。
As FPGAs are increasingly shared and remotely accessed by multiple users and third parties, they introduce significant security concerns. Modules running on an FPGA may include circuits that induce voltage-based fault attacks and denial-of-service (DoS). An attacker might configure some regions of the FPGA with bitstreams that implement malicious circuits. Attackers can also perform side-channel analysis and fault attacks to extract secret information (e.g., secret key of an AES encryption). In this paper, we present a convolutional neural network (CNN)-based defense to detect bitstreams of RO-based malicious circuits by analyzing the static features extracted from FPGA bitstreams. We further explore the criticality of RO-based circuits in order to detect malicious Trojans that are configured on the FPGA. Evaluation on Xilinx FPGAs demonstrates the effectiveness of the security solutions.