• 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.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.37-47
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• 2021

Today, people share information and communicate with others using various information and communication media such as e-mail, smartphones, SNS, etc. However, it is being used in malicious attacks to send a large amount of illegal spam or to use it for fraud by using illegally collected personal information and devices that are vulnerable to security. Illegal spam, smishing, and fraudulent mail(SCAM) cause a lot of direct and indirect damage to companies and users, including not only social costs such as mental fatigue, but also unnecessary consumption of IT infrastructure resources and economic losses. Although there are regulations related to spam, violators of the law are still on the rise by circumventing the law, and victims are constantly occurring, so it is necessary to review what the problem is. This study examined domestic and foreign spam-related regulations and spam-related response activities, identified problems, and suggested improvement countermeasures. Through this study, it was intended to suggest directions for improving spam-related systems in order to block illegal spam and prevent fraudulent damage.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.123-139
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• 2011

Web spam has a significant influence on the ranking quality of web search results because it promotes unimportant web pages. Therefore, web search engines need to filter web spam. web spam filtering is a concept that identifies spam pages - web pages contributing to web spam. TrustRank, Anti-TrustRank, Spam Mass, and Link Farm Spam are well-known web spam filtering algorithms in the research literature. The output of these algorithms depends upon the input seed. Thus, refinement in the input seed may lead to improvement in the quality of web spam filtering. In this paper, we propose seed refinement techniques for the four well-known spam filtering algorithms. Then, we modify algorithms, which we call modified spam filtering algorithms, by applying these techniques to the original ones. In addition, we propose a strategy to achieve better quality for web spam filtering. In this strategy, we consider the possibility that the modified algorithms may support one another if placed in appropriate succession. In the experiments we show the effect of seed refinement. For this goal, we first show that our modified algorithms outperform the respective original algorithms in terms of the quality of web spam filtering. Then, we show that the best succession significantly outperforms the best known original and the best modified algorithms by up to 1.38 times within typical value ranges of parameters in terms of recall while preserving precision.

• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.137-146
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• 2005

The email using internet is situated by means of basic communication method that ordinardy people use. Thereby damage scale of the spam mail becomes wider. The many blocking methods of the spam mail are proposed and archived. Hut they are insufficient to block various types of spam mail The blocking method of spam mail proposed by this paper is consisted of 3 steps (collection, frequency analysis and blocking). It can effectively block various types of spam mail using collected spam mail and various forms of spam mail that changes.

• The KIPS Transactions:PartC
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• v.13C no.6 s.109
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• pp.733-740
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• 2006

Spam Message has been Causing widespread damages on the Internet. One source of the problems is rooted from an anonymously posted message in the bulletin board in Internet communities. This type of the Spam messages tries to advertise products, to harm other's reputation, to deliver religious messages and so on. In this paper we present the Spam message filtering using the Bayesian approach. In order to increase usefulness of the Spam filter in the bulletin board in Internet communities, we made the Spam filter which can divide the Spam message into six categories such as advertisement, pornography, abuse, religion and other. The test conducted against messages posted on the popular web sites.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.10
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• pp.99-107
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• 2008

In this paper, we analyse the vulnerability of spam attacks and develop the Spamtester to confirm these spam attacks in SIP-based VoIP networks. Although there are several spam attacks on VoIP networks, the detail information for the SPIT is not enough to confirm the procedure and the result of spam attacks on VoIP networks. Specially, the spam attacks through abnormal process are difficult to trace the sender of spam. Also, it is not easy to impose the legal restriction to the spammer because of lack of information for the spam attack. Therefore, on VoIP networks, the possible scenario and detail procedure for VoIP spam is needed to be confirmed. This paper designes and implementes the spamtester, which is helpful to protect VoIP networks from the spam attacks.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.49-54
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• 2006

Recently, e-mail has become an important way of communications in IT societies, but it creates various social problems due to increase of spam mails. Even though many organizations and cooperation have been trying researches to develop spam mail blocking technologies, a lot of cost and system complexities are required because of varieties of spam blocking technologies. In this paper, we designed of the SMBC(Spam Mail Blocking Center) using the Fit FA(Filtering Algorithm) Finder. Fit-FA Finder that search and applises spam mail filtering algorithm of the most suitable confrontation according to type of spam mail. The system of spam mail filtering is decided performance of the system by procedure that spam filter is used. Go through designed Fit-FA Finder and reduced unnecessary filtering process and processing time and load than appointment order filter application way of existent spam mail interception system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3005-3027
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• 2015

Spam has become one of the top threats of micro-blogging networks as the representations of rumor spreading, advertisement abusing and malware distribution. With the increasing popularity of micro-blogging, the problems will exacerbate. Prior detection tools are either designed for specific types of spams or not robust enough. Spammers may escape easily from being detected by adjusting their behaviors. In this paper, we present a novel model to quantitatively evaluate information diffusion in micro-blogging networks. Under this model, we found that spam posts differ wildly from the non-spam ones. First, the propagations of non-spam posts mostly result from their followers, but those of spam posts are mainly from strangers. Second, the non-spam posts relatively last longer than the spam posts. Besides, the non-spam posts always get their first reposts/comments much sooner than the spam posts. With the features defined in our model, we propose an RBF-based approach to detect spams. Different from the previous works, in which the features are extracted from individual profiles or contents, the diffusion features are not determined by any single user but the crowd. Thus, our method is more robust because any single user's behavior changes will not affect the effectiveness. Besides, although the spams vary in types and forms, they're propagated in the same way, so our method is effective for all types of spams. With the real data crawled from the leading micro-blogging services of China, we are able to evaluate the effectiveness of our model. The experiment results show that our model can achieve high accuracy both in precision and recall.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.135-136
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• 2011

The more VoIP service is widely used, the more VoIP spam becomes threatened. Both VoIP spam violates the user's privacy and VoIP spam can cause money trouble. Therefore, it is important to reduce the VoIP spam but it is not easy to adopt some useful techniques to counter e-mail spam due to VoIP characteristics. We propose a technique using VoIP-RBL for countering SIP-based VoIP spam.

• 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.