• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Mobile Junk Message Filter Reflecting User Preference

Vol. 6, No.11, November 30, 2012
10.3837/tiis.2012.11.006, Download Paper (Free):

Abstract

In order to block mobile junk messages automatically, many studies on spam filters have applied machine learning algorithms. Most previous research focused only on the accuracy rate of spam filters from the view point of the algorithm used, not on individual users preferences. In terms of individual taste, the spam filters implemented on a mobile device have the advantage over spam filters on a network node, because it deals with only incoming messages on the users phone and generates no additional traffic during the filtering process. However, a spam filter on a mobile phone has to consider the consumption of resources, because energy, memory and computing ability are limited. Moreover, as time passes an increasing number of feature words are likely to exhaust mobile resources. In this paper we propose a spam filter model distributed between a users computer and smart phone. We expect the model to follow personal decision boundaries and use the uniform resources of smart phones. An authorized users computer takes on the more complex and time consuming jobs, such as feature selection and training, while the smart phone performs only the minimum amount of work for filtering and utilizes the results of the information calculated on the desktop. Our experiments show that the accuracy of our method is more than 95% with Na_ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.


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Cite this article

[IEEE Style]
Kyoung-Ju Lee and Deok-Jai Choi, "Mobile Junk Message Filter Reflecting User Preference," KSII Transactions on Internet and Information Systems, vol. 6, no. 11, pp. 2849-2865, 2012. DOI: 10.3837/tiis.2012.11.006

[ACM Style]
Lee, K. and Choi, D. 2012. Mobile Junk Message Filter Reflecting User Preference. KSII Transactions on Internet and Information Systems, 6, 11, (2012), 2849-2865. DOI: 10.3837/tiis.2012.11.006