Journal ArticleDOI
SMS Spam Detection Using Noncontent Features
TLDR
This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents.Abstract:
Short Message Service text messages are indispensable, but they face a serious problem from spamming. This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents.read more
Citations
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Proceedings ArticleDOI
Identifying Spam Patterns in SMS using Genetic Programming Approach
Dimple Sharma,Aakanksha Sharaff +1 more
TL;DR: The model proposed in this paper generates regular expressions as individuals of population, using Genetic Programming Approach, which is able to eliminate False Positive errors, thus saving legitimate messages from being misclassified.
Journal ArticleDOI
(Un/Semi-)supervised SMS text message SPAM detection
TL;DR: A content-based Bayesian classification approach which is a modest extension of the technique discussed by Resnik and Hardisty in 2010 is developed and is believed to be a useful tool for SMS SPAM detection.
Journal ArticleDOI
Differential evolution detection models for SMS spam
TL;DR: Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate, and surpasses the baseline methods.
Posted Content
On Detecting Messaging Abuse in Short Text Messages using Linguistic and Behavioral patterns.
TL;DR: This paper analyzes the effectiveness of machine learning filters based on linguistic and behavioral patterns in order to detect short text spam and abusive users in the network and explores different ways to deal with short text message challenges such as tokenization and entity detection by using text normalization and substring clustering techniques.
Journal Article
Novel Approach of Text Classification by SVM-RBF Kernel and Linear SVC
Gurvir Kaur,Er. Parvinder Kaur +1 more
TL;DR: In this paper working two learning approaches knn and support vector machine (SVM) yet SVM gives importance great exactness, accuracy, review than KNN, SVC.
References
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