scholarly journals Behavior anomaly detection based on big data analysis of Internet of Things

2021 ◽  
Vol 2004 (1) ◽  
pp. 012011
Author(s):  
Jinliang Yang ◽  
Xuan Lan ◽  
Liansheng Huang ◽  
Jigang Zeng
2017 ◽  
Vol 13 (7) ◽  
pp. 155014771772181 ◽  
Author(s):  
Seok-Woo Jang ◽  
Gye-Young Kim

This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.


2018 ◽  
Vol 24 (3) ◽  
pp. 1078-1094 ◽  
Author(s):  
Khalim Amjad Meerja ◽  
Praveen V. Naidu ◽  
Sri Rama Krishna Kalva

2020 ◽  
Vol 6 (4) ◽  
pp. 45-53
Author(s):  
Marimuthu Palaniswami ◽  
Aravinda S. Rao ◽  
Dheeraj Kumar ◽  
Punit Rathore ◽  
Sutharshan Rajasegarar

Author(s):  
D. R. Kolisnyk ◽  
◽  
K. S. Misevych ◽  
S. V. Kovalenko

The article considers the issues of system architecture IoT-Fog-Cloud, considers the interaction between the three levels of IoT, Fog and Cloud for the effective implementation of programs for big data analysis and cybersecurity. The article also discusses security issues, solutions and directions for future research in the field of the Internet of Things and nebulous computing.


2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xianzhi Tang ◽  
Chunyan Ding

The progress of the social economy and the rapid development of the power field have created more favorable conditions for the construction of my country’s power grid. In this network age, how to further realize the connection between the power system and the Internet of Things is the key content of many scholars’ research. In the Internet of Things environment, there have been many excellent results in the collection, storage, and management of electric power big data, but the problem of information security has not been completely solved. Based on big data analysis and Internet of Things technology, this paper studies the architecture design of power information security terminals. In view of the diverse types of power grid mobile information and the large amount of data, this paper designs a power transportation mobile information security management system structure, which improves the effective management of power data by the system through big data, smart sensors, and wireless communication technology. According to the experiment, the power information security terminal constructed in this paper can effectively reduce communication resources and save communication costs in the process of aggregating multidimensional data. In the user satisfaction survey, residents’ satisfaction with the convenience and safety of the intelligent power system is also as high as 9.312 and 9.233. On the whole, the application of big data and Internet of Things technology to the construction of power information security terminals can indeed improve the service efficiency of power companies under the premise of ensuring safety and allow users to have a better experience.


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