A Comprehensive Review on Email Spam Classification with Machine Learning Methods (original) (raw)

Survey of machine learning methods for spam e-mail classification

2020

The humongous volume of unsolicited bulk e-mail (spam) which is further increasing, is the major cause for developing antispam protection filters. Machine learning provides a very optimized approach to automatically filter spams at a very successful rate. Here, in this paper we survey some of the most popular machine learning algorithms (Naïve Bayes, k-NN, SVMs and ANN) and their applicability to the problem of spam e-mail classification. Descriptions of the algorithms are presented, and the comparison of their performance on the UCI spam-base dataset is presented. Keywords⸻ Spam, E-mail classification, Machine learning algorithms, k-NN, SVM, Naïve Bayes, ANN.

Spam Detection in Email using Machine Learning

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.

An Empirical Performance Comparison of Machine Learning Methods for Spam E-Mail Categorization

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.

Spam E-Mail Characterization: An Experimental Performance Comparison Of Machine Learning

2017

The increasing volume of unsolicited mass e-mail (otherwise called spam) has generated a need for reliable against spam filters.<br> Utilizing a classifier based on machine learning techniques to naturally filter out spam e-mail has drawn many researchers'<br> attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization,<br> which has been conducted with four machine learning calculations applied to different parts of e-mail. Experimental results<br> reveal that the header of e-mail provides very useful data for all the machine learning calculations considered to detect spam<br> e-mail.

Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends

arXiv (Cornell University), 2016

We present a comprehensive review of the most effective content-based e-mail spam filtering techniques. We focus primarily on Machine Learning-based spam filters and their variants, and report on a broad review ranging from surveying the relevant ideas, efforts, effectiveness, and the current progress. The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine Learning front in fighting spam. We conclude by measuring the impact of Machine Learning-based filters and explore the promising offshoots of latest developments.

Machine Learning Based Email Spam Detection: Achieving High Accuracy and Efficiency

International journal for research in applied science and engineering technology, 2024

Email communication has become an essential aspect of modern-day interactions, but the proliferation of spam emails poses significant challenges to users' productivity and security. This research paper presents a comprehensive study on the development and implementation of an efficient email spam detection and categorization system. The project aims to categorize emails into predefined sections by using the Support Vector Machine (SVM) model, Flask, and the Gmail API, ensuring accuracy and efficiency in email classification. The methodology involves data preparation, processing, storage, and management, ensuring robust security and privacy considerations. The system's three-tiered classification strategy enhances the accuracy of spam and ham detection. Future enhancements include integrating advanced machine learning models, user feedback mechanisms, and multi-platform support to adapt to evolving email trends and user preferences. This research contributes to the field of email management by offering a new approach to combat spam effectively and enhance email organization for users in the digital age.

Efficient Spam Email Classification using Machine Learning Algorithms

In today's digital age, since email is the main form of communication, the identification of email spam is a critical issue. In addition to consuming a lot of time and money, email spam is also a security and privacy risk. In this paper, we provide a means for email spam detection that employes machine learning Algorithms. The required features for training the ML models have been engineered after analysis of the email dataset of contentbased filtering obtained from Kaggle website. We tested a Several types of algorithms for machine learning and analyzed their level of performance using the dataset. Our findings demonstrate how effective is the suggested approach in identifying email spam with highest accuracy of 99.8% and Rmse of 0.2 .Here we applied , the various ML classifier algorithm such as Decision tree , Voting Classifier , Random Forest, Logistic Regression and so on to our dataset ,compared among each other and found which suits best for the dataset with the highest accuracy. This method can be useful in email clients or servers to detect spam emails automatically and enhanced

A Comprehensive Overview on Intelligent Spam Email Detection

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Spam, usually referred to as unsolicited commercial or bulk e-mail, has recently become a major issue on the internet. Time, storage, and transmission bandwidth are all wasted by spam. Spam email has been a growing issue for years. Nowadays, automatic email filtering appears to be the most successful strategy for preventing spam. Only several years ago most of the spam could be reliably dealt with by blocking e-mails coming from certain addresses or filtering out messages with certain subject lines. Spammers started employing a number of cunning strategies to get beyond filtering techniques, such as utilizing random sender addresses and/or adding random characters to the message subject line's beginning or conclusion. Machine learning techniques now a days are used to automatically filter the spam e-mail in a very successful rate. Machine learning field is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like human. Understanding, observing, and providing knowledge about a statistical occurrence are all terms used here. In the first place, data collection and representation are typically problem-specific (i.e., for email messages), and in the second place, e-mail feature selection and feature reduction aim to lower the dimensionality (i.e. the number of features).Finally, the e-mail classification phase of the process finds the actual mapping between training set and testing set. Machine Learning approach includes lots of algorithms that can be used in e-mail filtering like Naïve Bayes, K-nearest neighbour, Support VSector Machine, classifiers. In conclusion, we try to summarize the performance results of the few machine learning methods in terms of spam precision and accuracy.

E-mail Spam Detection and Classification using SVM

2020

here we present an inclusive review of recent and successful content-based e-mail spam filtering techniques. Our focus is majorly on machine learning-based spam filters and variants which inspired from them. We report on relevant ideas, techniques, major efforts, and the state-of-the-art in the field. The initial interpretation of the prior work shows the basics of e-mail spam filtering and feature engineering. In this we conclude by studying techniques, methods, evaluation benchmarks, and explore the promising offshoots of latest developments and suggest lines of future investigations. Keywords—— SVM Classifier, Spam Email Classification, Data Mining, Data Science, Machine Learning.

MACHINE LEARNING METHODS FOR SPAM E-MAIL CLASSIFICATION

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.