• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.47-49
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• 2009

본 연구에서는 이메일 사용자별로 제공받은 사용자 선호 정보 대상으로 EM 클러스터링을 수행하여 사용자 클러스터를 만든 후, 사용자 클러스터들의 이메일 반응 분포를 계산함으로써 사용자 취향에 따라 동일한 이메일에 대해서도 서로 다른 반응을 가질 수 있다는 사실을 확인하려고 한다. 그 결과로부터 현재의 내용기반 방식과는 다르게 사용자 선호도를 고려한 스팸 메일 필터 구축 방법을 제안한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• 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:PartC
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• v.12C no.3 s.99
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• pp.361-368
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• 2005

Internet mail has become a common communication method around the world because of tremendous Internet service usage increment. In other respect, Most Internet users' mail addresses are exposed to spammer, and the damage of Junk mail is growing bigger and bigger. These days, Junk mail delivery problem is becoming more serious, because this is used for an attack or propagation scheme of malicious code. It's a most dangerous dominant cause for computer system accident. This paper shows the Junk mail filtering model and implementation which is based on FQDN (Fully Qualified Domain Name) check and evaluates it for proposing advanced scheme against Junk mail.

• 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.

• Annual Conference on Human and Language Technology
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• 2007.10a
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• pp.122-127
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• 2007

최근 대표적인 1인 미디어의 형태인 블로그는 개인 기록의 수단뿐만 아니라 기업의 홍보에까지 널리 사용되는 인터넷 미디어이다. 그러나 누구나 글을 쓸 수 있다는 자유로움 이면에 이를 이용한 덧글 스팸이 성행이 성행하고 있다. 일반적인 스팸 필터의 경우 그 해당 덧글만을 가지고 스팸 필터링을 한다. 그러나 특성상 스팸인 덧글이 정상인 덧글보다 상대적으로 짧기 때문에 일반적인 덧글 자체만의 필터링 방법으로는 높은 정확도를 기대하기 힘든 단점이 있다. 본 논문에서는 정상인 덧글과 본문간의 내용상의 유사도가 있음을 가정해 이런 정보를 역카이제곱 분류기에 동시출현(co-occurrence) 정보로 부여함으로써 스팸 필터의 정확도를 높이고자 했으며, 실제 그러한 정보를 추가함으로 단순한 확률기반 스팸 필터링 방법을 사용하는 것보다 스팸 필터의 전반적인 성능이 상승되었음을 실험 결과를 통해 알 수 있었다.

• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.213-219
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• 2006

본 논문에서는 스팸메일에 나타나는 스팸성 자질과 URL 자질을 이용한 최대엔트로피모델 기반 스팸 필터 시스템을 제안한다. 스팸성 자질은 스패머들이 스팸메일에 인위적으로 넣는 강조 패턴이나 필터 시스템을 통과하기 위해 비정상적으로 변형시킨 단어들을 말한다. 스팸성 자질 외에 반복적으로 나타나는 URL과 비정상적인 Ink도 자질로 사용하였다. 메일 수신자에게 추가적인 정보 제공을 목적으로 하이퍼링크로 연결시키거나 메일에 직접 타이핑한 URL 중 필터 시스템을 피하기 위해 유효하지 알은 비정상적인 URL들이 스팸 메일을 걸러내는데 도움을 줄 수 있기 때문이다. 또한 스팸성 자질과 URL을 각각 적용한 두 분류기를 통합하였다. 분류기의 통합은 각 분류기에 이용된 자질을 독립적으로 사용할 수 있다는 장점을 가지고 있다. 실험 결과를 통해 스팸성 자질과 URL을 이용함으로써 스팸 필터 시스템의 성능을 향상시킬 수 있음을 확인할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.343-346
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• 2002

스팸메일이 급증함에 따라 신뢰할 수 있는 전자메일 필터의 요구가 늘어나는 추세다. 스팸메일을 보내는 스패머(spammer)의 거의 대부분은 광고가 주요 목적이다. 멀티미디어(multimedia)기반의 전자메일은 정보전달 및 시각효과가 뛰어나 스패머가 선호하는 전자메일의 한 형태이다. 이런 종류의 전자메일은 텍스트 기반(基盤) 스팸메일 필터의 성능을 떨어뜨리거나 필터링을 아예 불가능하게 한다. 본 연구에서 발신지(發信地) 추적기법과 사례기반학습을 이용해 신뢰할 수 있는 한국어 스팸 메일필터를 설계 및 구현하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.576-579
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• 2009

The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. Therefore, it is more required to classify incoming e-mails efficiently and accurately. Currently, the e-mail classification techniques are focused on two way classification to filter spam mails from normal ones based mainly on Bayesian and Rule. The clustering method has been used for the multi-way classification of e-mails. But it has a disadvantage of low accuracy of classification. In this paper, we propose a novel multi-way e-mail classification method that uses PCA for automatic category generation and dynamic category hierarchy for high accuracy of classification. It classifies a huge amount of incoming e-mails automatically, efficiently, and accurately.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.419-425
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• 2009

The amount of incoming e-mails is increasing rapidly due to the wide usage of Internet. Therefore, it is more required to classify incoming e-mails efficiently and accurately. Currently, the e-mail classification techniques are focused on two way classification to filter spam mails from normal ones based mainly on Bayesian and Rule. The clustering method has been used for the multi-way classification of e-mails. But it has a disadvantage of low accuracy of classification and no category labels. The classification methods have a disadvantage of training and setting of category labels by user. In this paper, we propose a novel multi-way e-mail hierarchy classification method that uses PCA for automatic category generation and dynamic category hierarchy for high accuracy of classification. It classifies a huge amount of incoming e-mails automatically, efficiently, and accurately.