email spam filtering with machine learning
Author(s):
Parminder kaur
Keywords:
Email spam, machine learning, spam detection, Naive Bayes, Support Vector Machines, decision trees, deep learning, Term Frequency-Inverse Document Frequency, N-grams, Logistic Regression, Synthetic Minority Oversampling Technique, data preprocessing.
Abstract
Email spam, often known as junk email, comprises unsolicited and irrelevant messages sent in bulk. These emails can range from promotional content to malicious links and phishing attempts, posing significant risks to recipients. The adverse effects of spam are both social and economic, impacting individuals and organizations by reducing productivity and increasing security threats. It explores the application of machine learning techniques for effective spam detection. Machine learning, a subset of artificial intelligence, has demonstrated superior capabilities in identifying spam through pattern recognition and adaptation to new spam tactics. The study leverages various algorithms, including Naive Bayes, Support Vector Machines (SVMs), decision trees, and deep learning approaches, to enhance the accuracy and scalability of spam filters. The methodology involves collecting and preprocessing data from multiple sources, including the Enron Email Dataset and the SpamAssassin Public Corpus. Feature extraction techniques such as Term Frequency-Inverse Document Frequency (TF-IDF) and N-grams are employed to distinguish spam from legitimate emails. The research addresses class imbalance through techniques like oversampling and Synthetic Minority Oversampling Technique (SMOTE). Evaluation of the developed models highlights Logistic Regression as an effective tool for binary classification in spam filtering. The results demonstrate a high accuracy rate, with significant potential for reducing false negatives and improving email security. This study underscores the importance of advanced machine learning approaches in mitigating the pervasive issue of email spam, aiming to enhance user experience and organizational productivity.
Article Details
Unique Paper ID: 166692

Publication Volume & Issue: Volume 11, Issue 2

Page(s): 1638 - 1646
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