Comparative Analysis of Detection of Email Spam With the Aid of Machine Learning Approaches. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Madhavan, Mangena Venu; Pande, Sagar; Umekar, Pooja; Mahore, Tushar; Kalyankar, Dhiraj
• Is Part Of: IOP conference series. Volume 1022:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1022/1/012113
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Over the past few decades, Technology has gained a rapid pace in its development making communication easier. Considering several modes of communication, E-mails(Electronic mails) are the best means for both informal and formal conversations. Some also use e-mails to store and share important information in the form of text, images, documents, etc. between people using electronic devices. Besides, some people improperly use this means of communication by sending useless or unwanted e-mails in bulk i.e., spammed emails which could result in disproportionate usage of memory in the mailbox. There are many suggested approaches in practice that could identify spam emails from the mailbox using machine learning methods. This paper mainly deals with the comparative analysis of detecting Spam Emails by various machine learning methodologies along with the proposed methodology. Considering various evaluation metrics such as Accuracy, Error, Evaluation time, Efficiency, and so on for the evaluation of models. This document draws the contrast on strengths, drawbacks, and limitations of some of the existing techniques that use the approaches of machine learning to detect spam emails. The machine learning method is further resourceful than the acquaintance approach of engineering which does not involve the specifications of any instructions. Considering various evaluation metrics such as Accuracy, Error, Evaluation time, Efficiency, and so on for the evaluation of models. The various accuracies obtained in this framework are KNN – 96.20%, Naïve Bayes – 99.46%, SVM – 96.90, Rough Sets Classifiers – 97.42%.