An Adaptive Wolf Based Dansing System for Securing Hadoop at the Data Cleaning Stage

An Adaptive Wolf Based Dansing System for Securing Hadoop at the Data Cleaning Stage
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Saritha Gattoju, Vadlamani Naga Lakshmi
DOI :  10.14445/22315381/IJETT-V70I4P204

How to Cite?

Saritha Gattoju, Vadlamani Naga Lakshmi, "An Adaptive Wolf Based Dansing System for Securing Hadoop at the Data Cleaning Stage," International Journal of Engineering Trends and Technology, vol. 70, no. 4, pp. 31-43, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P204

Abstract
Nowadays a large amount of data is available for the association of authority using business decisions. Moreover, the collected data from various resources are too noisy, which affects the prediction results and accuracy. Hence, Data cleaning has been introduced to provide better data quality, but the main issues of data cleaning are time consumption and malicious attacks. In this paper, a novel Wolf based Wide Dansing System (WbWDS) is developed to provide security for data during the cleaning stage. Hence, the novel WbWDS is designed with four layers: logical, physical, execution, and data cleaning.
Furthermore, wolf fitness is updated to the developed framework for enhancing the security function. In addition, the involvement of wolf fitness has afforded the finest continuous monitoring results of malicious events. Additionally, the proposed WbWDS technique is implemented in Python, and an attack is launched in the cleaning layer to check the developed method`s reliability. Finally, achieved performance metrics of developed WbWDS are compared with existing methods and gained the finest results with outstanding confidential rate and low execution time.

Keywords
Attacks detection, Confidentiality measure, Data cleaning, Secure Hadoop application.

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