IMS: Intelligent Hardware Monitoring System for Secure SoCs
Wadid Foudhaili, Aykut Rencber, Anouar Nechi, Rainer Buchty, Mladen Berekovic, Andres Gomez, Saleh Mulhem

TL;DR
This paper introduces IMS, a neural network-based hardware module integrated into RISC-V SoCs, capable of real-time detection of AXI protocol violations to enhance security with minimal performance overhead.
Contribution
The paper presents a novel, lightweight neural network-based hardware monitoring system for real-time AXI protocol violation detection in SoCs, demonstrating high accuracy and low latency.
Findings
Achieved 98.7% detection accuracy with <=3% latency overhead.
Implemented on AMD Zynq UltraScale+ MPSoC with minimal hardware footprint.
Demonstrated feasibility for resource-constrained edge environments.
Abstract
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…
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Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Security and Verification in Computing · Cryptographic Implementations and Security
