PAAFDA: Inclusive Data Fudging Detection Algorithm

Authors

  • Mr. P. Prashanth Kumar, V. Supraja ,K. Pranathi ,E. Naveena

Keywords:

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Abstract

Modern technology has elevated data and its analysis from the status of scattered spreadsheet values and characteristics to that of a tool to revolutionize any major industry. It is critical to create a reliable system that can detect and properly highlight all instances of corrupted data in the dataset, as data fudging may originate from many different unethical and unlawful sources. A difficult challenge is the detection of damaged data and the recovery of data from a corrupted dataset. Unless this is handled early on, it could cause issues when processing data using machine or deep learning techniques later on.

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References

E. Burgdorf, Predicting the impact of data fudging on the operation of cyber physical systems. 2017. [2] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009.

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Published

2023-12-24

How to Cite

Mr. P. Prashanth Kumar, V. Supraja ,K. Pranathi ,E. Naveena. (2023). PAAFDA: Inclusive Data Fudging Detection Algorithm . Pegem Journal of Education and Instruction, 13(4), 462–471. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4008

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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