Logistic Regression Based Classification of Spam and Non-Spam Emails

Khanday, Shahbaz Ahmad and Parveen, Suraiya (2021) Logistic Regression Based Classification of Spam and Non-Spam Emails. In: ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India.

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An email client receives emails from different websites, portals and domains, which can be an advertisement. Receiving a bulk amount of emails can cause serious damages like suspension of a particular email id. Mostly an email client gets exposed to the number of malicious receipts by registering an email account to a web portal, which in turn sends a bulk amount of emails. One of the solutions to escape from spam emails is to develop a decision based system which can classify the spam and non-spam emails. This can be achieved using different machine learning and deep learning and deep learning algorithms to classify the spam and non-spam emails by accessing the received emails of an email client. The machine learning approaches and mechanisms like SVM, naive Bayesian classifier, artificial neural networks and random forests can be of important help to determine spam emails. After classifying a spam email source a user can navigate, block and report the source of the spam email generator like spam-bots

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: machine learning decision tree support vector machine (svm) logistic regression artificial neural networks naive bayesian classifier and spam-bots
Subjects: T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 06 May 2021 09:34
Last Modified: 06 May 2021 09:34
URI: https://eprints.eudl.eu/id/eprint/2887

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