Identification of Dominant Spam Email Features to Improve Detection Accuracy of Machine Learning Algorithms

Hegde, Sujay (2021) Identification of Dominant Spam Email Features to Improve Detection Accuracy of Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

The current business environment and personal use of internet shoots larger numbers of individuals and companies, falling prey to phishing attacks and spam emails. The growing dependence on internet allows the cybercriminals to hatch nasty plans against internet users, by releasing spam mails, with attractive contents and convincing them to fall victims to such incidences. In simpler terms, spam emails are unsolicited commercial/bulk e-mails, which have become a big cause of concern for the users of emails and for that matter, the internet.

The application of spam feature detection will be able to influence the nature of prediction models that can be utilized further for the detection of other effective datasets apart from spam. The identification and recognition of the most dominant feature of spam will be efficient in ensuring that the spam developing system is being disclosed which will further contribute to the research process of the spam detection process.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	19 Dec 2022 16:25
Last Modified:	07 Mar 2023 17:21
URI:	https://norma.ncirl.ie/id/eprint/6005

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