Drift Phenomenon based Email Spam Prediction with LSTM and GRU Approach

Rana, Monika (2023) Drift Phenomenon based Email Spam Prediction with LSTM and GRU Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

This paper explores the complexities of drift in email spam and addresses the use of deep learning models to predict and filter spam emails that use the smart tactics. It provides an overview of the effects of these tactics on the efficiency of traditional spam filters and the need for advanced methods to identify and mitigate evolving spam threats. In this research two deep learning models Long Short-Term Memory networks (LSTMs) and Gated recurrent Unit (GRU) is used. The suggested models are made to capture temporal patterns, dynamic content structures, and contextual data that traditional filters find challenging to recognise. The deep learning models learn to recognize underlying patterns and adjust to the changing character of spam by being trained on labelled datasets including both traditional and drifting spam emails. The effectiveness of the suggested deep learning models in predicting and categorising spam emails with drift is analysed by experimental findings.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Siddig, Abubakr UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > Electronic Mail T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > Electronic Mail Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	28 Dec 2024 15:34
Last Modified:	28 Dec 2024 15:34
URI:	https://norma.ncirl.ie/id/eprint/7257

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