Email Spam Detector Research Paper

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

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.

2020

Today there are more than 4.39 billion internet users that almost 70 percent of them use social media on mobile devices. Network security is one of the most important aspects to consider while working over the internet. E-mail is one of the most secure medium for online communication and transferring data or messages through the web. This paper illustrates an email spam detection method using natural language processing and machine learning techniques (MLT). Then we present the classification and evaluation results.

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.

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.

Springer, 2022

Spam has emerged as a significant issue that is endangering the reliability of existing email networks. The email has become an AQ1 essential means of sharing information worldwide for personal or commercial purposes. For this reason, creating an effective spam filter is one AQ2 of our biggest challenges. Considering this demand, we build a dynamic spam filter that can filter the standard message and spam messages more efficiently using the most common Naive Bayesian algorithm. Our pro-AQ3 posed model works mainly by considering the content of messages. We used a supervised machine learning model, which contains primarily two phases: Training and Testing. We build a model based on the Bayesian concept in the training phase. In the testing phase, we test our messages by dividing them into words and sentences and calculating their probability for both spam and non-spam categories. Finally, the highest probability value is our desired result and deployed as a web application. Our suggested spam filtering model achieved 98% accuracy. It worked well on both online and offline systems.

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

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

World Journal of Advanced Engineering Technology and Sciences, 2024

Recent research indicates a notable surge in SMS spam, posing as entities aiming to deceive individuals into divulging private account or identity details, commonly termed "phishing" or "email spam". Conventional spam filters struggle to adequately identify these malicious emails, leading to challenges for both consumers and businesses engaged in online transactions. Addressing this issue presents a significant learning challenge. While initially appearing as a straightforward text classification problem, the classification process is complicated by the striking similarity between spam and legitimate emails. In this study, we introduce a novel method named "filter" designed specifically for detecting deceptive SMS spam. By incorporating features tailored to expose the deceptive techniques employed to dupe users, we achieved an accurate classification rate of over 99.01% for SMS spam emails, while maintaining a low false positive rate. These results were attained using a dataset comprising 746 instances of spam and 4822 instances of legitimate emails. The filter's accuracy, evaluated on a dataset with two attributes and 5568 instances, notably surpasses existing methodologies. Our proposed model, a Hybrid NB-ANN model, achieves the highest accuracy at 99.01%, outperforming both Naïve Bayes (98.57%) and Artificial Neural Network (98.12%). This highlights the efficacy of the hybrid approach in enhancing accuracy for email spam detection and malware filtering, ensuring comprehensive coverage across training and test datasets for improved feedback loops.