Spam Email Detection Using Machine Learning Techniques

Sakarya Üniversitesi Fen Bilimleri Enstitüsü dergisi/Sakarya Üniversitesi fen bilimleri enstitüsü dergisi, 2023

Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83%. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature. 1. Introduction With the widespread use of the Internet, electronic communication has become more preferred. One of the most important tools of electronic communication is electronic messages, which we call e-mail. Today, individuals or organizations have one or more email accounts. Instant delivery of messages, no cost and ease of use increase the importance and prevalence of e-mail [1]. According to Statista Research Department data, the number of actively used e-mail accounts in 2020 is more than 4 billion. This number is estimated to increase to 4.6 billion in 2025. In 2020, 306 billion e-mails are sent and received every day, and this number is expected to exceed 376 billion in 2025 [2]. The use of e-mail is not only practical but also has various vulnerabilities. The e-mail account to be hijacked in various ways, for e-mails containing advertisements etc. to hijack your computer by installing a software on your computer when you click on the advertisement, and for the installed software to disrupt communication by sometimes filling the

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.

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.

IRJET, 2022

Nowadays, Email spam has become a big problem, with the fast growth of internet users, email spams are also increasing. People are using them for phishing, illegal and unethical practices and frauds. Sending malicious links through spam emails that can harm for our system and may also get into your system. It is very simple for spammers to create a fake profile and email account, they show like a real person in their spam emails, these spammers simply target people who are not aware of these frauds. then there is a need to identify those spam mails which are frauds, this project will identifies those spams using techniques of machine learning, this paper will discuss machine learning algorithm's and apply all these algorithm's to our dataset. it select the best algorithm, for this project algorithm will be chosen based on the best accuracy and precision in email spam detecting.

IJISTECH (International Journal of Information System and Technology), 2021

The rapid development of email use and the convenience provided make email as the most frequently used means of communication. Along with its development, many parties are abusing the use of email as a means of advertising promotion, phishing and sending other unimportant emails. This information is called spam email. One of the efforts in overcoming the problem of spam emails is by filtering techniques based on the content of the email. In the first study related to the classification of spam emails, the Naïve Bayes method is the most commonly used method. Therefore, in this study researchers will add Random Forest and K-Nearest Neighbor (KNN) methods to make comparisons in order to find which methods have better accuracy in classifying spam emails. Based on the results of the trial, the application of Naïve bayes classification algorithm in the classification of spam emails resulted in accuracy of 83.5%, Random Forest 83.5% and KNN 82.75%

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.

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 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.

Background: As people using social media increases the data generation also increases and the data generated may be safe or unsafe. If we see some applications like Twitter and mail. We get a lot of emails or twits that include all dangerous and useful things. Here to be safe from the threats and dangers we need a filter that separates useful messages from spam and helps us not to drown in a trap. And one of the approaches to do this is explained in this paper. In this paper, the algorithm followed is the Naïve Bayes classifier. This also provides the comparison between using Naïve Bayes, KNN, and Logistic Regression to solve the same problem that is spam filtering and term frequency-inverse document frequency (TFIDF).

Applied computing and informatics, 2022

Purpose-In this digital era, email is the most pervasive form of communication between people. Many users become a victim of spam emails and their data have been exposed. Design/methodology/approach-Researchers contribute to solving this problem by a focus on advanced machine learning algorithms and improved models for detecting spam emails but there is still a gap in features. To achieve good results, features also play an important role. To evaluate the performance of applied classifiers, 10-fold cross-validation is used. Findings-The results approve that the spam emails are correctly classified with the accuracy of 98.00% for the Support Vector Machine and 98.06% for the Artificial Neural Network as compared to other applied machine learning classifiers. Originality/value-In this paper, Point-Biserial correlation is applied to each feature concerning the class label of the University of California Irvine (UCI) spambase email dataset to select the best features. Extensive experiments are conducted on selected features by training the different classifiers.