Ren, X. (2015) Since an Attacker Will Use JTAG Differently from a Legitimate User, It Is Possible to Detect the Difference Using Machine-Learning Algorithms (Moni-toring JTAG Access Patterns).

has been cited by the following article:

• TITLE: AI-Driven JTAG Log Monitoring for FPGA

  AUTHORS: Raj Parikh, Khushi Parikh

  KEYWORDS: LSTMs, JTAG (Joint Test Action Group), JTAG Log Monitoring, AI-Driven Debugging, FPGA Anomaly Detection, ASIC Security, Hybrid Cloud Computing, Predictive Failure Analysis

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.4, April 22, 2025

  ABSTRACT: The FPGA and ASIC debugging, boundary scan testing, and device coding owe vivid gratitude to JTAG Interfaces (Joint Test Action Group format adhering largely to IEEE 1149.1 standards). In this paper, we experiment with an AI-based method for JTAG log monitoring and performance trend forecasting. Using deep learning models such as LSTMs and Transformers, the system can find deviations from log patterns and predict potential failures in advance. This kind of closed-loop analysis enhances our reliability to unprecedented levels. The system described in this work is made for hybrid cloud deployment, providing secure, scalable, and real-time log analysis software. The paper further discusses the architectural integration of AI into existing JTAG frameworks for FPGAs and RISC-V ASICs, detailing security considerations, implementation challenges, and potential industry applications. This paper is based on a patent: AI-Driven Hybrid Cloud JTAG Log Monitoring System for FPGA Debugging and Failure Prediction (Patent Number 63/771,667).