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Divya, P.
- Applications and Techniques of Outlier Detection
Abstract Views :191 |
PDF Views:2
Authors
P. Divya
1,
M. Devapriya
1
Affiliations
1 Government Arts College, Coimbatore, IN
1 Government Arts College, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 5 (2012), Pagination: 265-269Abstract
Outlier detection plays a vital role in many data-mining applications. Noisy data is a significant problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. A research on crime and terrorist activities are some of the application domains are being researched with strict confidentiality. The techniques and results of such techniques are not readily forthcoming. In this paper, various outlier detection techniques are discussed in a structured and generic description. With this exercise, the researchers have attain a better understanding of the different directions of research on outlier analysis. Then, the applications of outlier detection are also screened out in a better and precise way. This paper will be useful for beginners in this research field.Keywords
Outliers, Outlier Detection, Outlier Applications.
- Data Provenance for Multi-Hop Internet of Things
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, Nandha College of Technology, Erode, IN
1 Department of Information Technology, Nandha College of Technology, Erode, IN
Source
Data Mining and Knowledge Engineering, Vol 11, No 2 (2019), Pagination: 21-26Abstract
Security protocols for Internet of Things (IoT) need to be light weighted due to limited resources and scalability are available in the internet. Small and low-energy devices are suitable for cryptographic solution because of their energy and space limitations. The Received Signal Strength Indicator (RSSI) is used to generate link fingerprints for communicating IoT nodes. Correlation Coefficient is used for matching the link fingerprint. Co-relation Coefficient is used to communicating secured data transfer. Adversarial Node can be detected foe specific link in between the two nodes. Data Provenance has achieved by comparison of packet header.
Keywords
Light Weight, Data Provenance, Fingerprint, Adversarial Node, Received Signal Strength Indicator (RSSI).References
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