Academy & Industry Research Collaboration Center (AIRCC)

Volume 11, Number 07, May 2021

A Study of Identifying Attacks on Industry Internet of Things Using Machine Learning


Chia-Mei Chen1, Zheng-Xun Cai1, Gu-Hsin Lai2, 1National Sun Yat-sen University, Taiwan, 2Taiwan Police College, Taiwan


The “Industry 4.0” revolution and Industry Internet of Things (IIoT) has dramatically transformed how manufacturing and industrial companies operate. Industrial control systems (ICS) process critical function, and the past ICS attacks have caused major damage and disasters in the communities. IIoT devices in an ICS environment communicate in heterogeneous protocols and the attack vectors might exhibit different misbehavior patterns. This study proposes a classification model to detect anomalies in ICS environments. The evaluation has been conducted by using ICS datasets from multiple sources and the results show that the proposed LSTM detection model performs effectively.


Industry Internet of Things, Machine Learning, Anomaly Detection.