Header Based Email Spam Detection Framework Using Support Vector Machine (SVM) Technique

2019 ◽  
pp. 57-65 ◽  
Author(s):  
Siti Aqilah Khamis ◽  
Cik Feresa Mohd Foozy ◽  
Mohd Firdaus Ab Aziz ◽  
Nordiana Rahim
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Spam Detection ◽  
Email Spam

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

LSSVM-Based Social Spam Detection Model

2013 ◽  
Vol 765-767 ◽  
pp. 1281-1286
Author(s):  
Xiao Lei Yang ◽  
Yi Dan Su ◽  
Jin Ping Mo
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Linear Equations ◽  
Detection Methods ◽  
Support Vector ◽  
Spam Detection ◽  
Squared Error Loss Function ◽  
Detection Model ◽  
Squared Error ◽  
Empirical Function

To Resolve the garbage tag issue in Folksonomy, Lssvm algorithm for social spam detection model (least Squares support vector machine classifiers) was proposed. The method of inequality change the constraints in the traditional support vector machine into equality constraints, and take the empirical function of the squared error loss function as the Experience function in training set. so that the quadratic programming problem convert QP into solving linear equations, it was improving solution the speed of solution and accuracy of convergence.The experimental results show that we have got higher classification accuracyand less predict time than traditional svm detection methods based on least squares support vector machine algorithm garbage tag detection model.

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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