Independent and Personal SMS Spam Filtering

Nuruzzaman, Muhammad Taufiq; Lee, Changmoo; Choi, Deokjai

doi:10.1109/cit.2011.23

Cited by 43 publications

(24 citation statements)

References 9 publications

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“…For empirical evaluation we combined the tagged SMS datasets published by Almeida et.al., [12], Nuruzzaman et.al., [27] and Delany et.al., [18]. Since all these datasets were constructed using NUS SMS corpus [16] and Grumbletext [2] they have many duplicates.…”

Section: Empirical Measurementsmentioning

confidence: 88%

“…It is known from previous works that that probabilistic graphical models based algorithms (like Bayesian filters) can be effectively (in terms of power and memory consumption) deployed on resource constrained mobile devices [27]. Based on this premise, we develop a stacked classifier and evaluate its efficacy in filtering SMS spam messages.…”

Section: Introductionmentioning

confidence: 98%

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The curse of 140 characters

Narayan

Saxena

2013

Proceedings of the Third ACM Workshop on Security and Privacy in Smartphones &Amp; Mobile Devices

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Many applications are available on Android market place for SMS spam filtering. In this paper, we conduct a detailed study of the methods used in spam filtering in these applications by reverse engineering them. Our study has three parts. First, we perform empirical tests to valuate accuracy and precision of these apps. Second, we test if we can use email spam classifiers on short text messages effectively. Empirical test results show that these email spam classifiers do not yield optimal accuracy (like they do on emails) when used with SMS data. Finally, in this work we develop a two-level stacked classifier for short text messages and demonstrate the improvement in accuracy over traditional Bayesian email spam filters. Our experimental results show that spam filtering precision and accuracy of nearly 98% (which is comparable with those of email classifiers) can be obtained using the stacked classifier we develop.

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Section: Empirical Measurementsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 98%

The curse of 140 characters

Narayan

Saxena

2013

Proceedings of the Third ACM Workshop on Security and Privacy in Smartphones &Amp; Mobile Devices

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“…Yadav et al [26] also grouped each word in both English and Hindi SMSs based on the Ham and Spam classes. Nuruzzaman et al [27] had also grouped individual letters from words and categorised them based on the Ham and Spam classes. Almeida et al [19] identified two sets of token and calculated the occurrences of each token respectively.…”

Section: Lemmatizationmentioning

confidence: 99%

“…It is one of the more effective techniques used to classify text documents [16]. The reason Naive Bayes is applied is because this technique is one of the simplest probability techniques that can predict the class that contains better results [24], [27]. Taufiq Nuruzzaman et al [15] integrated Bayesian filtering to develop a simple tool to filter SMS Spam.…”

Section: Classification a Plethora Of Classification Techniques Have mentioning

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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“…There has been numerous numbers of studies on active learning for text classification using machine learning techniques [9]- [11], probabilistic models [12], [13]. The query by committee algorithm (Seung et al 1992, Freund et al, 1997) used priori distribution than hypothesis.…”

Section: Background Study and Related Workmentioning

confidence: 99%

SMS Classification Based on Naïve Bayes Classifier and Apriori Algorithm Frequent Itemset

Ahmed¹,

Guan²,

Chung³

2014

IJMLC

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Abstract-In this paper, we propose a hybrid system of SMS classification to detect spam or ham, using Naï ve Bayes classifier and Apriori algorithm. Though this technique is fully logic based, its performance will rely on statistical character of the database. Naï ve Bayes is considered as one of the most effectual and significant learning algorithms for machine learning and data mining and also has been treated as a core technique in information retrieval. However, by applying user-specified minimum support and minimum confidence, we gain significant improvement on effective accuracy 98.7% from the traditional Naï ve Bayes approach 97.4% experimenting on UCI Data Repository.Index Terms-Short message service (SMS), Naï ve Bayes classifier, Apriori algorithm, spam, ham, minimum support, minimum confidence.

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Independent and Personal SMS Spam Filtering

Cited by 43 publications

References 9 publications

The curse of 140 characters

The curse of 140 characters

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

SMS Classification Based on Naïve Bayes Classifier and Apriori Algorithm Frequent Itemset

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