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DOI: 10.14569/IJACSA.2022.0131040
PDF

Detection of Severity-based Email SPAM Messages using Adaptive Threshold Driven Clustering

Author 1: I V S Venugopal
Author 2: D Lalitha Bhaskari
Author 3: M N Seetaramanath

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The classification of emails is one crucial part of the email filtering process, as emails have become one of the key methods of communication. The process for identifying safe or unsafe emails is complex due to the diversified use of the language. Nonetheless, most of the parallel research outcomes have demonstrated significant benchmarks in identifying email spam. However, the standard processes can only identify the emails as spam or ham. Henceforth, a detailed classification of the emails has not been achieved. Thus, this work proposes a novel method for the identification of the emails into various classes using the proposed deep clustering process with the help of the ranking of words into severity. The proposed work demonstrates nearly 99.4% accuracy in detecting and classifying the emails into a total of five classes.

Keywords: BoW collection; web crawler; email text extraction; subsetting method; email class detection; ranking method

I V S Venugopal, D Lalitha Bhaskari and M N Seetaramanath, “Detection of Severity-based Email SPAM Messages using Adaptive Threshold Driven Clustering” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131040

@article{Venugopal2022,
title = {Detection of Severity-based Email SPAM Messages using Adaptive Threshold Driven Clustering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131040},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131040},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {I V S Venugopal and D Lalitha Bhaskari and M N Seetaramanath}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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