scholarly journals Email Spam Detection using SVM

2021 ◽  
Vol 9 (VI) ◽  
pp. 669-672
Author(s):  
Azhar Baig
Keyword(s):  
Support Vector Machines ◽  
Personal Information ◽  
Support Vector ◽  
Spam Detection ◽  
Large Numbers ◽  
Vector Machines ◽  
E Mail ◽  
Email Spam

E-mail contributes to internet messaging as a necessary component. Spam mails are unwanted messages that appear in large numbers and are exploited by spammers to divulge personal information of the user. These e-mails are frequently company/control announcements or malware that the user receives suddenly. Email spamming is one of the Internet's unsolved challenges, causing inconvenience to users and loss to businesses. Filtering is one of the foremost widely used and important methods for preventing spam emails. Email filters are commonly wont to organize incoming emails, protect computers from viruses, and eliminate spam. We present this method to classifying spam emails using support vector machines during this study, the SVM outperformed other classifiers.

Using of support vector machines for link spam detection

2011 ◽  
Author(s):  
Ruslan V. Sharapov ◽  
Ekaterina V. Sharapova
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Spam Detection ◽  
Vector Machines

scholarly journals A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques

2021 ◽  
Author(s):  
Simarjeet Kaur ◽  
Meenakshi Bansal ◽  
Ashok Kumar Bathla
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Spam Detection ◽  
Learning Techniques ◽  
E Mail ◽  
Email Spam

Due to the rise in the use of messaging and mailing services, spam detection tasks are of much greater importance than before. In such a set of communications, efficient classification is a comparatively onerous job. For an addressee or any email that the user does not want to have in his inbox, spam can be defined as redundant or trash email. After pre-processing and feature extraction, various machine learning algorithms were applied to a Spam base dataset from the UCI Machine Learning repository in order to classify incoming emails into two categories: spam and non-spam. The outcomes of various algorithms have been compared. This paper used random forest, naive bayes, support vector machine (SVM), logistic regression, and the k nearest (KNN) machine learning algorithm to successfully classify email spam messages. The main goal of this study is to improve the prediction accuracy of spam email filters.

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