MACHINE LEARNING METHODS FOR SPAM E-MAIL CLASSIFICATION

2004

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Using a classifier based on machine learning techniques to automatically filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning algorithms applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful information for all the machine learning algorithms considered to detect spam e-mail.

IRJET, 2020

Worldwide email is a common and fast communicating way and relatively low sending cost for message transfer protocol. But sometimes without filtering mail box are fill-up with unsolicited bulk email and junk email that is known as spam email. Many financial transaction and electronic business contribute or promote their business through email, which is very annoying to users. The use of spam email is rapidly increasing day after day. For that reason, filtering is essential and popular one to stop spam email. ML approaches are given more successful rate to filtering the spam email. In our paper, we give an overview some of ml classification algorithms as K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Multilayer perception (MLP) are used for learning the features of spam emails. By using the confusion matrix on 10-fold cross-validation in this paper to compare the performance of those six ML classifiers based on accuracy, recall & precision. The main goal of this article is to determine the better spam classification techniques for spam detection.

Computer Engineering and Intelligent Systems, 2020

Emails are essential in present century communication however spam emails have contributed negatively to the success of such communication. Studies have been conducted to classify messages in an effort to distinguish between ham and spam email by building an efficient and sensitive classification model with high accuracy and low false positive rate. Regular rule-based classifiers have been overwhelmed and less effective by the geometric growth in spam messages, hence the need to develop a more reliable and robust model. Classification methods employed includes SVM (support vector machine), Bayesian, Naïve Bayes, Bayesian with Adaboost, Naïve Bayes with Adaboost. However, for this project, the Bayesian was employed using Python programming language to develop a classification model.

figshare. Conference contribution., 2022

In today's world, email is used in almost every industry, from business to education. Emails can be categorized into two categories: ham and spam. Junk emails, also known as spam messages, are emails that have been designed to harm recipients by wasting their time, computing resources, and stealing their valuable information. It is estimated that spam emails are increasing at a rapid rate. One of the most important and prominent spam prevention techniques is filtering email. Naive Bayes, Decision Trees, Neural Networks, and Random Forests are among the methods used for this purpose by researchers. In this project, I examine the Logistic Regression machine learning model for spam filtering in email by categorizing messages into appropriate groups. This study also compares the techniques based on accuracy, precision, recall, etc. The accuracy level for this project was around 97%. Towards the end, these insights and future research directions, and challenges are outlined.

Currently, the Internet E-mail infrastructure has become very significant and most popular used for communication between end user, E-commerce and academic research purposes due to it is rapid, inexpensive and very active. This E-mail organizational structures is used for the daily work. Sometime we receive many undesirable E-mails from different unknown resource. These unwanted E-mails are identified as Spam E-mails. The determination of Ham and Spam E-mail is a main target and a variety of algorithms of classification have been implemented. The complication of a classifier algorithm is substantially reduced if the numbers of features in Spam E-mail data set are reduced. In this paper, it is proposed to present some of the most common data mining algorithms J48, Support Vector Machine (SVM) and Naive Bayes for Spam E-mail classification problem. The standard dataset Spam base is used. Enhanced the Spam Email classification is impact thereof and is objective of our study. An experimental study is carried out to build up a classifier Spam E-mail standard dataset that includes Ham and Spam E-mail message. A Rough Set Theory (RST) and Symmetric uncertainty (SU) methods are utilized to minimize dimensionality of Spam E-mail data group. The sub features got by the RST and Symmetric uncertainty are employed to train and test the different classifiers. A comparison of obtained results between by reduced features set and original data set are presented. The obtained results show that the effectiveness of classifiers with the reduced features has outperformed the existing systems.

International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 2023

In today's era, almost everyone is using emails on their daily basis. In our proposed research, we suggest a machine learning-based strategy for enhancing email spam filters' accuracy. Traditional rule-based filters have grown less effective as spam emails have multiplied exponentially. Models can be trained to identify emails as spam or not using machine learning algorithms, particularly supervised learning. We need to create a simple and straightforward machine learning model in order to reach more accurate results while categorizing email spam. We picked the Naive Bayes technique for our model since it is quicker and more accurate than other algorithms. The suggested method can have incorporated into current email systems to enhance spam filtering functionality. This review paper provides an overview of the machine learning model we have suggested.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

This comprehensive review delves into the realm of email spam classification, scrutinizing the efficacy of various machine learning methods employed in the ongoing battle against unwanted email communication. The paper synthesizes a wide array of research findings, methodologies, and performance metrics to provide a holistic perspective on the evolving landscape of spam detection. Emphasizing the pivotal role of machine learning in addressing the dynamic nature of spam, the review explores the strengths and limitations of popular algorithms such as Naive Bayes, Support Vector Machines, and neural networks. Additionally, it examines feature engineering, dataset characteristics, and evolving threats, offering insights into the challenges and opportunities within the field. With a focus on recent advancements and emerging trends, this review aims to guide researchers, practitioners, and developers in the ongoing pursuit of robust and adaptive email spam classification systems.

2020

In today's world, bulk of emails is received by every individual out of which many fraudulent or spam emails are also present. The task of a good email service provider is to create an algorithm so that such fraudulent or spam messages are automatically detected and then they are sent to the spam folder. In this paper, the authors proposed a novel technique by which this sorting of email can be done automatically. Using machine learning method, the authors implemented a method in which spam mail and fraudulent messages have been successfully detected and those mails have been sent to the spam folder of the mailbox. The authors, in this paper, presented the description of the algorithm along with the test results.

World Academy of Research in Science and Engineering, 2019

Data Science is an emerging multidisciplinary field which employs algorithms, processes, scientific methods to extract information and insights in various forms which is both structures and unstructured much similar to data mining and prediction analysis. Advertisement and bulk emails, also called as spam, makes an estimate of 62% of the Worldwide internet traffic. Since 1978, when first unwanted mail was sent, technology have advanced but still the detection of spams remains a chronophagous and big budget problem in the field of mathematical sciences. The current study evaluates the effectiveness and efficiency of various machine learning techniques which include K-NN, Decision tree, random forest, Naive Bayes and SVM for spam detection. A data set comprising of 962 emails containing both genuine emails and spams has been used in this study. Some deep learning techniques for classification of spams is also suggested for better performance.

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL

Email is one of the most economical and fast communication means in recent years; however, there has been a high increase in the rate of spam emails in recent times due to the increased number of email users. Emails are mainly classified into spam and non-spam categories using data mining classification techniques. This paper provides a description and comparative for the evaluation of effective classifiers using three algorithms - namely k-nearest neighbor, Naive Bayesian, and support vector machine. Seven spam email datasets were used to conducted experiment in the MATLAB environment without using any feature selection method. The simulation results showed SVM classifier to achieve a better classification accuracy compared to the K-NN and NB.