• Journal of Digital Contents Society
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• v.16 no.5
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• pp.815-822
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• 2015

By the growing of SMS phishing victims, applications for processing spam messages are being released in succession. However most spam messages that cleverly modified the content like separating the consonants and vowels are fail to be filtered. In this paper, we implemented an application 'AntiSpam' which is able to identify spam strings in the text message to solve this problem. 'AntiSpam' searches spam strings in the text message by using set-based POI search algorithm, and then calculate the possibility of whether it is spam or not in accordance with the search results. In addition, it catches skillfully disguised spam messages in order to avoid missing the spam filtering. Users, who received a message, can check the result in spam message possibility decision result and the contents of the message and they can choose how to handling the message.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2849-2865
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• 2012

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 user's 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 user's 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$\ddot{i}$ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.57-64
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• 2017

Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.122-135
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• 2011

Mobile spam message is a harmful factor which makes receivers to be annoyed and leads to unnecessary social cost. Unwanted junk messages flowing to a smart phone ruin main purpose of the smart work system to enhance the productivity, so we need to study on this area. In this paper, we proposed a novel spam filter on the smartphone in order to reduce computing process and improve the accuracy rate by feedback of error results to a training sample set. As the spam classifier operates on the smartphone independently with training on only user's received data, it could reflect user preference. The authorized personal computer takes on heavy works, such as preprocessing, feature selecting and training process, and the smartphone takes on light works to block junk messages. Experimental results showed reasonable accuracy rate of over 95%, and we found that the application occupied constant computing resources while running on the phone.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.271-276
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• 2014

Short message service(SMS) in a mobile communication environment is a very convenient method. However, it caused a serious side effect of generating spam messages for advertisement. Those who send spam messages distort or deform SMS sentences to avoid the messages being filtered by automatic filtering system. In order to increase the performance of spam filtering system, we need to recover the distorted sentences into normal sentences. This paper proposes a method of normalizing the various types of distorted sentence and extracting keywords through automatic word spacing and compound noun decomposition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9B
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• pp.908-913
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• 2009

Mobile phones became important household appliance that cannot be without in our daily lives. And the short messaging service (SMS) in these mobile phones is 1.5 to 2 times more than the voice service. However, the spam filtering functions installed in mobile phones take a method to receive specific number patterns or words and recognize spam messages when those numbers or words are present. However, this method cannot properly filters various types of spam messages currently dispatched. This paper proposes a more powerful and more adaptive spam filtering system using SVM and thesaurus. The system went through a process of isolating words from sample data through pro-processing device and integrating meanings of isolated words using a thesaurus. Then it generated characteristics of integrated words through the chi-square statistics and studied the characteristics. The proposed system is realized in a Window environment and the performance is confirmed through experiments.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.194-196
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• 2010

휴대폰의 광범위한 보급으로 문자메시지의 사용이 급증하고 있다. 이와 동시에 사용자가 원하지 않는 광고성 스팸문자도 넘쳐나고 있다. 본 연구는 이러한 스팸문자메시지를 자동으로 판별하는 시스템을 개발하는 것이다. 우리는 기계학습방법인 지지벡터기계(Support Vector Machine)을 사용하여 시스템을 학습하였으며 자질의 선택은 카이제곱 통계량을 이용하였다. 실험결과 F1 척도로 약 95.5%의 정확률을 얻었다

• Information Systems Review
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• v.20 no.1
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• pp.61-80
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• 2018

Individuals often receive smishing campaigns (mobile phishing messages), which they treat as spam. Thus, firms should understand how their customers distinguish their promotion messages from smishing. However, only a few studies examined this important issue. The present study employs the elaboration likelihood model to develop research hypotheses on the relationship between message cue and message credibility. The message cue in this study is classified as content cue, which is found in the content of promotion messages, and as leakage cue, which is found in peripheral information in the message. Leakage cue includes orthography (inclusion of special characters)and an abbreviated link sent by a faithless sender. We also propose that contextualization has a moderating effect on the relationship between content cue and credibility. We conducted a survey experiment to examine the effect of message cues on message credibility in the context of respondents receiving discount coupons through mobile messages. The result of data analysis based on 166 responses suggests that leakage cue had a negative effect on message credibility. A message with defective content cue has a marginally negative effect on message credibility. In particular, defective content cue in a high-contextual message has a strong negative impact on message credibility. This effect was not observed in low-contextual messages. Moreover, message credibility is significantly low regardless of the degree of contextualization if there is a leakage cue in the message. Our findings suggest that mobile promotion messages should be customized for message receivers and should have no leakage cues.