• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.45-56
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• 2009

There are many internet users who collect the public opinions and express their opinions for internet news or blog articles through the replying comment on online community. But, it is hard to search and explore useful messages on web blogs since most of web blog systems show articles and their comments to the form of sequential list. Also, spam and malicious comments have become social problems as the internet users increase. In this paper, we propose a clustering and visualizing system for responding comments on large-scale weblogs, namely 'Daum AGORA,' using similarity analysis. Our system shows the comment clustering result as a simple screen view. Our system also detects spam comments using Needleman-Wunsch algorithm that is a well-known algorithm in bioinformatics.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.853-862
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• 2003

Many more use e-mail purely on a personal basis and the pool of e-mail users is growing daily. Also, the amount of mails, which are transmitted in electronic commerce, is getting more and more. Because of its convenience, a mass of spam mails is flooding everyday. And yet automated techniques for learning to filter e-mail have yet to significantly affect the e-mail market. This paper suggests Web Mail Filtering Agent for Personalized Classification, which automatically manages mails adjusting to the user. It is based on web mail, which can be logged in any time, any place and has no limitation in any system. In case new mails are received, it first makes some personal rules in use of the result of observation ; and based on the personal rules, it automatically classifies the mails into categories according to the contents of mails and saves the classified mails in the relevant folders or deletes the unnecessary mails and spam mails. And, we applied Bayesian Algorithm using Dynamic Threshold for our system's accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.129-137
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• 2008

In this paper, we propose an improved Bayesian Filtering mechanism to reduce the False Positives that occurs in the existing Bayesian Filtering mechanism. In the existing Bayesian Filtering mechanism, the same Bayesian Filtering DB trained at the e-mail server is applied to each e-mail user. Also, the training method using receiving e-mails only could not provide the high quality of ham DB. Due to these problems, the existing Bayesian Filtering mechanism can produce the False Positives which misclassify the ham e-mails into the spam e-mails. In the proposed mechanism, the sending e-mails of the user are treated as the high quality of ham information, and are trained to the Bayesian ham DB automatically. In addition, by providing a different Bayesian DB to each e-mail user respectively, more efficient e-mail filtering service is possible. Our experiments show the improvement of filtering accuracy by 3.13%, compared to the existing Bayesian Filtering mechanism.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.64-71
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• 2006

Electronic mail (E-mail) is an integral part of communication for the recent Internet users. However, e-mail has also come to serve as a means to support flood of unwanted spam mails and junk mails having bad purposes. This paper was conducted in order to develop an intelligent e-mail system using user behavior pattern that can prevent these unnecessary information and enable the user to enjoy communication via e-mail in a cleaner environment. The concentrated analysis of the user behavior in terms of using e-mail functions has resulted in better classification between unnecessary and necessary information, thereby facilitating faster disposal of spam mails.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.552-554
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• 2003

전자우편은 기존 우편 기능을 대체하는 대표적인 정보 전달 수단으로 자리 잡고 있다. 전자매일 사용자의 증가에 따라 망은 기업들은 전자 메일을 통해 광고를 하게 되었다. 이에 따라 전자매일 사용자들은 인터넷 상에 개인 전자메일 주소가 노출됨으로 많은 스팸메일을 수신하게 되는데, 이것은 전자메일 사용자에게 많은 부담이 되고있다. 본 논문은 전자우편 문서내의 단어들을 대상으로 통계적 방법의 SVM을 이용하여 스팸메일을 필터링 하였으며, 학습 단계에서 단어 자질공간의 축소를 위해 DF값 변화에 따른 학습을 통하여 분류의 성능을 비교하였다. SVM의 성능 평가를 위해 확률적 방법의 나이브 베이지안과 벡터 모텔을 이용한 분류기와 성능을 비교함으로써 SVM 방법이 우수한 성능을 보임을 검증하였다.

• Annual Conference of KIPS
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• 2009.11a
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• pp.745-746
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• 2009

오늘날 사람들의 의견을 제시하는 공간은 폐쇄적인 인쇄물이나 수동적인 답변 수준을 벗어나 무한의 공간을 가지는 웹에서 이루어지고 있다. 불특정 다수를 대상으로 하며 정형화된 틀을 없는, 더욱 유용한 의견을 많이 얻을 수 있는 특징을 가졌기 때문에, 이를 위해 오피니언 마이닝에 대한 연구가 활발히 진행되고 있다. 기본적으로 오피니언 마이닝은 해당 분야에 대한 정확한 정보를 찾는 것을 목적으로 하지만, 그러한 정보를 제외한 나머지 부분에 대해서도 충분히 유용하게 사용할 수 있다. 본 논문에서는 그 나머지 부분을 이용하여 무분별하게 등록되고 있는 스팸성 댓글을 효과적으로 필터링 할 수 있는 방법을 제안한다.

• Journal of Advanced Navigation Technology
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• v.15 no.2
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• pp.287-297
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• 2011

As electronic mails are being widely used for facility and speedness of information communication, as the amount of spam mails which have malice and advertisement increase and cause lots of social and economic problem. A number of approaches have been proposed to alleviate the impact of spam. These approaches can be categorized into pre-acceptance and post-acceptance methods. Post-acceptance methods include bayesian filters, collaborative filtering and e-mail prioritization which are based on words or sentances. But, spammers are changing those characteristics and sending to avoid filtering system. In the case of Korean, the abnormal usages can be much more than other languages because syllable is composed of chosung, jungsung, and jongsung. Existing formal expressions and learning algorithms have the limits to meet with those changes promptly and efficiently. So, we present an methods for recognizing Korean abnormal language(Koral) to improve accuracy and efficiency of filtering system. The method is based on syllabic than word and Smith-waterman algorithm. Through the experiment on filter keyword and e-mail extracted from mail server, we confirmed that Koral is recognized exactly according to similarity level. The required time and space costs are within the permitted limit.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.409-416
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• 2006

In this paper, we propose an algorithm for extracting text regions from spam-mail images using color layer. The CLTE(color layer-based text extraction) divides the input image into eight planes as color layers. It extracts connected components on the eight images, and then classifies them into text regions and non-text regions based on the component sizes. We also propose an algorithm for recovering damaged text strokes from the extracted text image. In the binary image, there are two types of damaged strokes: (1) middle strokes such as 'ㅣ' or 'ㅡ' are deleted, and (2) the first and/or last strokes such as 'ㅇ' or 'ㅁ' are filled with black pixels. An experiment with 200 spam-mail images shows that the proposed approach is more accurate than conventional methods by over 10%.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.261-265
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• 2008

As increasing the user of VoIP service using ITSP(Internet Telephony Service Provider), the VoIP spam becomes a big problem. The spam used in the existing public telephone is detected by using the pattern inspection of call behavior because it is difficult to filter contents for the characteristic of real-time voice communication. However there is a false-positive problem. The threat on spam remains where spam with low threshold can't be detected or users share one number. In this paper, we propose anti-spam for VoIP based on luring test. The proposed method gives a user luring test and he/she can connect to a receiver if passing turing test. A ticket is given to a user that pass luring test and it reduces overhead of luring test in re-dial. The proposed method is implemented on ASUS WL-500G wireless router and Asterisk IP-PBX. Experimental results show the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.211-214
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• 2003

The problem of unsolicited e-mail has been increasing for years, so many researchers has studied about spam filtering and prevention. In this article, we proposed a faster spam prevention algorithm based on userID instead of full email address. But there are 2% of false-negatives by userID. In this case, we store those domains in a DB and filter them out. The proposed algorithm requires small DB and 3.7 times faster than the e-mail address comparison algorithm. We implemented this algorithm using SPRSW(Spam Prevention using Replay Secrete Words) to register userID automatically in userID DB.