kumoh national institute of technology
Networked Systems Lab.

Fabliha Bushra Islam, Md. Sajjad Hossain, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, and Jae-Min Lee " Machine Learning Based Network Intrusion Detection in Cyber Physical System ", 2021 Winter Conference on Korea Institute Of Communication Sciences(KICS), Feb. 3-5, 2021, YongPyeong Resort, Pyeongchang Kun, Gangwon Province, Korea (N8 and N12)
By : Bushra
Date : 2021-01-05
Views : 37

Abstract—This paper represents Machine Learning Classification based anomaly detection technique to observe pernicious activity of network traffic characteristics in Cyber Physical System (CPS). The architecture utilizes a sufficient number of classification algorithms applying in real time malicious collection combining from popular IoT devices, SK NUGU and EZVIZ WiFi camera, and revealing malicious performances. It employs a successive training process evaluated from an adequate number of classification techniques to achieve the accuracy based on Intrusion Detection. This application can be dynamic perception into the explanation of future malevolent and malicious exercises in Smart Manufacturing Industries, Industry IoT technologies, and Smart healthcare management.


Index Terms—Machine Learning (ML), Cyber Physical System (CPS), Internet of Things (IoT), MATLAB


 
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