用机器学习实时检测FPGA恶意比特流，无需源码即可发现隐藏攻击。
Real-time ML-based Defense Against Malicious Payload in Reconfigurable Embedded Systems
- 直接分析比特流二进制特征，不依赖源码或网表。
- 随机森林模型在122个样本上达到0.97的宏F1分数。
- 轻量级模型可部署于PYNQ平台，适合嵌入式系统实时防护。
FPGA在可重构系统中的广泛应用带来了安全风险，恶意比特流可能引发拒绝服务、数据泄露或隐蔽攻击。本文研究了嵌入式系统中的芯片级硬件恶意载荷，提出一种基于监督学习的检测方法，通过静态字节级特征识别恶意比特流。该方法直接在二进制层面分析比特流，无需源码或网表，实现无需解密的实时检测。数据来自SOTA基准并针对Xilinx PYNQ-Z1开发板重构建模，共包含122个良性与恶意配置样本。采用字节频次分析向量化，结合TSVD降维与SMOTE处理类别不平衡。评估表明，随机森林分类器取得0.97的宏F1得分，验证了在资源受限系统中实时木马检测的可行性。最终模型经序列化后成功部署于PYNQ平台，实现集成比特流分析。
原文摘要 · Abstract (English)
The growing use of FPGAs in reconfigurable systems introducessecurity risks through malicious bitstreams that could cause denial-of-service (DoS), data leakage, or covert attacks. We investigated chip-level hardware malicious payload in embedded systems and proposed a supervised machine learning method to detect malicious bitstreams via static byte-level features. Our approach diverges from existing methods by analyzing bitstreams directly at the binary level, enabling real-time detection without requiring access to source code or netlists. Bitstreams were sourced from state-of-the-art (SOTA) benchmarks and re-engineered to target the Xilinx PYNQ-Z1 FPGA Development Board. Our dataset included 122 samples of benign and malicious configurations. The data were vectorized using byte frequency analysis, compressed using TSVD, and balanced using SMOTE to address class imbalance. The evaluated classifiers demonstrated that Random Forest achieved a macro F1-score of 0.97, underscoring the viability of real-time Trojan detection on resource-constrained systems. The final model was serialized and successfully deployed via PYNQ to enable integrated bitstream analysis.
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