Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber SystemsNonparametric Test for Change-Point Detection of IoT Time-Series Data

A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems:... [The Internet of Things is a concept of computer networks consisting of devices interacting with each other without human intervention. The complete or partial absence of human participation in the operation of such a network makes it necessary to solve the problem of automatic recognition of a significant deviation of the current state of the network from its normal state. The concept of “significance” is vague until it is formalized with the help of rigorous statistical concepts, in particular, the concept of confidence intervals with a given level of significance. In this chapter, we offer a new effective online algorithm for detection of change-points in data generated by IoT devices (tracking data, health rate data etc.). This allows detecting failures, cyber attacks and other deviations in the data. We describe a non-parametric test for evaluation of the statistical hypothesis that data in two adjacent time intervals of a time series have the same distribution. When this hypothesis is true, we detect a change-point. The significance level for the test is less than 0.05. The test is universal in two aspects: it permits ties in a sample and it save the sensitivity when the distributions are greatly overlapped. We demonstrate the prevalence of the proposed test over widely used the Kolmogorov–Smirnov test and the Wilcoxon signed-rank tests using both artificial samples with various distributions and properties (disjoint, slightly overlapped, and strongly overlapped) and real examples. We show that our approach provides robust, sensitive and accurate results.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber SystemsNonparametric Test for Change-Point Detection of IoT Time-Series Data

Part of the Intelligent Systems Reference Library Book Series (volume 210)
Editors: Kumar, Pardeep; Obaid, Ahmed Jabbar; Cengiz, Korhan; Khanna, Ashish; Balas, Valentina Emilia
Klyushin, Dmitriy; Urazovskyi, Andrii
A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems — Aug 24, 2021
Download PDF
23 pages

Loading next page...
 
/lp/springer-journals/a-fusion-of-artificial-intelligence-and-internet-of-things-for-oUY26J200H
Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-030-76652-8
Pages
99 –122
DOI
10.1007/978-3-030-76653-5_5
Publisher site
See Chapter on Publisher Site

Abstract

[The Internet of Things is a concept of computer networks consisting of devices interacting with each other without human intervention. The complete or partial absence of human participation in the operation of such a network makes it necessary to solve the problem of automatic recognition of a significant deviation of the current state of the network from its normal state. The concept of “significance” is vague until it is formalized with the help of rigorous statistical concepts, in particular, the concept of confidence intervals with a given level of significance. In this chapter, we offer a new effective online algorithm for detection of change-points in data generated by IoT devices (tracking data, health rate data etc.). This allows detecting failures, cyber attacks and other deviations in the data. We describe a non-parametric test for evaluation of the statistical hypothesis that data in two adjacent time intervals of a time series have the same distribution. When this hypothesis is true, we detect a change-point. The significance level for the test is less than 0.05. The test is universal in two aspects: it permits ties in a sample and it save the sensitivity when the distributions are greatly overlapped. We demonstrate the prevalence of the proposed test over widely used the Kolmogorov–Smirnov test and the Wilcoxon signed-rank tests using both artificial samples with various distributions and properties (disjoint, slightly overlapped, and strongly overlapped) and real examples. We show that our approach provides robust, sensitive and accurate results.]

Published: Aug 24, 2021

Keywords: IoT; Time series; Change-point problem; Tracking data; Health rate control; Nonparametric test; Homogeneity measure

There are no references for this article.