In the modern Systems-on-Chip (SoC), the Advanced eXtensible Interface (AXI) protocol exhibits security vulnerabilities, enabling partial or complete denial-of-service (DoS) through protocol-violation attacks. The recent countermeasures lack a dedicated real-time protocol semantic analysis and evade protocol compliance checks. This paper tackles this AXI vulnerability issue and presents an intelligent hardware monitoring system (IMS) for real-time detection of AXI protocol violations. IMS is a hardware module leveraging neural networks to achieve high detection accuracy. For model training, we perform DoS attacks through header-field manipulation and systematic malicious operations, while recording AXI transactions to build a training dataset. We then deploy a quantization-optimized neural network, achieving 98.7% detection accuracy with <=3% latency overhead, and throughput of >2.5 million inferences/s. We subsequently integrate this IMS into a RISC-V SoC as a memory-mapped IP core to monitor its AXI bus. For demonstration and initial assessment for later ASIC integration, we implemented this IMS on an AMD Zynq UltraScale+ MPSoC ZCU104 board, showing an overall small hardware footprint (9.04% look-up-tables (LUTs), 0.23% DSP slices, and 0.70% flip-flops) and negligible impact on the overall design's achievable frequency. This demonstrates the feasibility of lightweight, security monitoring for resource-constrained edge environments.

翻译：在现代片上系统（SoC）中，高级可扩展接口（AXI）协议存在安全漏洞，可通过违反协议的攻击实现部分或完全拒绝服务（DoS）。现有防护措施缺乏专用的实时协议语义分析，且规避了协议合规性检查。本文针对此AXI漏洞问题，提出一种用于实时检测AXI协议违规的智能硬件监控系统（IMS）。IMS是一种利用神经网络实现高检测精度的硬件模块。在模型训练阶段，我们通过报头字段篡改和系统性恶意操作实施DoS攻击，同时记录AXI事务以构建训练数据集。随后部署量化优化的神经网络，实现了98.7%的检测准确率，延迟开销≤3%，推理吞吐量>250万次/秒。我们将该IMS作为内存映射IP核集成至RISC-V SoC中，以监控其AXI总线。为进行演示并为后续ASIC集成提供初步评估，我们在AMD Zynq UltraScale+ MPSoC ZCU104开发板上实现了该IMS，结果显示其硬件占用面积较小（查找表（LUT）9.04%、DSP切片0.23%、触发器0.70%），且对整体设计可达到的工作频率影响可忽略不计。这证明了在资源受限的边缘环境中实现轻量级安全监控的可行性。