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.
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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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Patil, S., Nadimpalli, S.V., Yadav, P.K. (2019). Email Authorship Attribution. In: Saini, H., Singh, R., Kumar, G., Rather, G., Santhi, K. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-13-3765-9_47
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DOI: https://doi.org/10.1007/978-981-13-3765-9_47
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