Open AccessA Study of Identifying Attacks on Industry Internet of Things Using Machine Learning
Author(s) -
Chia-Mei Chen,
Zheng-Xun Cai,
Gu-Hsin Lai
Publication year - 2021
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110707
Subject(s) - industrial internet , internet of things , computer science , the internet , process (computing) , industrial control system , computer security , function (biology) , industrial revolution , manufacturing , control (management) , artificial intelligence , world wide web , business , operating system , marketing , evolutionary biology , political science , law , biology
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.