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Email Authorship Attribution

  • Suman PatilEmail author
  • Sandeep Varma Nadimpalli
  • Pavithra K. Yadav
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 65)

Abstract

Email correspondence is regularly manhandled for directing social designing assaults including spamming, phishing, data fraud, and circulating malware. This is to a great extent credited to the issue of obscurity intrinsic. Finding the authorship of email which can be stated as attribution problem is contemplated as content classification issue where the styles of writing of people are displayed in view of their already composed documents. In this paper, Multiple Association Rules for Authorship Attribution (CMARAA) is proposed to solve the problem of authorship attribution. The proposed method makes the remarkable composition style of features of a person. The experimental evaluation shows that CMARAA classifies with 92% of accuracy being the most accurate algorithm when compared to other classification algorithms for different combinations of authors.

Keywords

Authorship attribution Email classification Association mining Stylometric features 

Notes

Acknowledgements

The authors would like to acknowledge and thank technical education quality improvement program TEQIP phase III, BMS College of Engineering for funding this research work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Suman Patil
    • 1
    Email author
  • Sandeep Varma Nadimpalli
    • 1
  • Pavithra K. Yadav
    • 1
  1. 1.Department of Information Science and EngineeringBMS College of EngineeringBengaluruIndia

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