A New SMS Spam Detection Method Using Both Content-Based and Non Content-Based Features

Sulaiman, Nurul Fadhilah; Jali, Mohd Zalisham

doi:10.1007/978-3-319-24584-3_43

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Five different algorithms and three different datasets were used to verify the proposed method. The results indicated that the proposed method could produce a reasonable detection rate [12].…”

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confidence: 88%

Differential evolution detection models for SMS spam

Hameed

2021

IJECE

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With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. 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. Moreover, it surpasses the baseline methods.

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confidence: 88%

Differential evolution detection models for SMS spam

Hameed

2021

IJECE

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“…are three dataset that used by Nurul and Mohd [8]. They consider that numbers and symbols in dataset should not be cleaned because it may help in the detection process beside the SMS length and keywords.…”

Section: British English Sms Corpora (Bec) Uci Machine Learning (Ucimentioning

confidence: 99%

“…From the literature, the most widely accepted technique and the prominent ones is Bayesian filters that applied by[3][4][5][8][9]. However, the second most prominent technique used by researchers was Support Vector Machine as its applied by[3][7][8]…”

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confidence: 99%

A survey of Emerging Techniques in Detecting SMS Spam

Alqahtani

Alghazzawi

2019

TMLAI

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In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

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“…However, the message recipients might feel uncomfortable when receiving unwanted advertisement message from an unknown sender. This unwanted message is known as SMS Spam [2]. The mobile device is also at risk because of Spam attacks.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

Foozy

Ahmad

Abdollah

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. SMS Spamming is a serious attack that can manipulate the use of the SMS by spreading the advertisement in bulk. By sending the unwanted SMS that contain advertisement can make the users feeling disturb and this against the privacy of the mobile users. To overcome these issues, many studies have proposed to detect SMS Spam by using data mining tools. This paper will do a comparative study using five machine learning techniques such as Naïve Bayes, K-NN (K-Nearest Neighbour Algorithm), Decision Tree, Random Forest and Decision Stumps to observe the accuracy result between RapidMiner and WEKA for dataset SMS Spam UCI Machine Learning repository.

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A New SMS Spam Detection Method Using Both Content-Based and Non Content-Based Features

Cited by 6 publications

References 10 publications

Differential evolution detection models for SMS spam

Differential evolution detection models for SMS spam

A survey of Emerging Techniques in Detecting SMS Spam

A Comparative Study with RapidMiner and WEKA Tools over some Classification Techniques for SMS Spam

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