TY - GEN
T1 - Supervised Machine Learning for Modbus Communication Protocol Decoding
AU - Reid, Skyler
AU - Marceau, Maximus
AU - Filler, Keith
AU - Mecham, Keith D.
AU - Whitaker, Bradley M.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.
AB - This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.
KW - Automatic decoding
KW - Machine learning
KW - Modbus RTU
KW - Serial communication
UR - https://www.scopus.com/pages/publications/105010174388
U2 - 10.1109/IETC64455.2025.11039366
DO - 10.1109/IETC64455.2025.11039366
M3 - Conference contribution
AN - SCOPUS:105010174388
T3 - 2025 Intermountain Engineering, Technology and Computing, IETC 2025
BT - 2025 Intermountain Engineering, Technology and Computing, IETC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 Intermountain Engineering, Technology and Computing, IETC 2025
Y2 - 9 May 2025 through 10 May 2025
ER -