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TF-IDF classification based Multinomial Naïve Bayes model for spam filtering

Chavez, Alan (2020) TF-IDF classification based Multinomial Naïve Bayes model for spam filtering. Masters thesis, Dublin, National College of Ireland.

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Email spam, better known as unwanted email messages, is the practice of sending unsolicited electronic messages with different intentions, commonly commercial purposes, or trying to commit criminal actions. Despite the numerous anti-spam measures nowadays, spam still being a problem all over the internet due to the low-cost and high impact that represents elaborate a spam campaign. Many different solutions exist to categorize incoming messages such as white list, grey list, blacklist, Machine Learning, Rule-based filtering, etc. However, no one definitively. A possible reason is since spammers are high resilient, once a spam filtering method is compromised spammers adapt to it. The aim of the present work has the objective of detecting in a more effective way spam email with the Multinomial Naïve Bayes approach, in addition to text sanitation and TF-IDF. Results given by the proposed model gives an accuracy improve than Multinomial Naïve Bayes by its own.

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
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 26 Jan 2021 15:09
Last Modified: 26 Jan 2021 15:09

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