• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.238-240
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• 2004

정보 통신 기술의 발달로 엄청난 양의 정보가 World Wide Web을 통해 저장되고 공유된다. 특히, 사용자가 WWW을 이용하여 필요한 정보를 얻고자할 때, 가장 많이 사용되는 것이 Web search engine이다. 그러나 Web search engine의 algorithm 자체의 부정확성과 악의적으로 작성된 Web page로 인해 search engine 결과가 사용자의 요구와 일치하지 못하는 문제가 발생한다. 본 논문에서는 여러 Web search algorithm 중에서 Web structure management 기법을 중심으로 문제점을 분석하고 이를 해결할 수 있는 수정된 algorithm을 제시한다. 마지막으로 제시된 algorithm이 spamming page를 filtering하는 과정을 예시하여 논증한다.

• Journal of KIISE:Databases
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• v.33 no.7
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• pp.708-714
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• 2006

The World Wide Web has become an entrenched global medium for storing and searching information. Most people begin at a Web search engine to find information, but the user's pertinent search results are often greatly diluted by irrelevant data or sometimes appear on target but still mislead the user in an unwanted direction. One of the intentional, sometimes vicious manipulations of Web databases is Web spamming as Google bombing that is based on the PageRank algorithm, one of the most famous Web structuring techniques. In this paper, we regard the Web as a directed labeled graph that Web pages represent nodes and the corresponding hyperlinks edges. In the present work, we define the label of an edge as having a link context and a similarity measure between link context and the target page. With this similarity, we can modify the transition matrix of the PageRank algorithm. A motivating example is investigated in terms of the Singular Value Decomposition with which our algorithm can outperform to filter the Web spamming pages effectively.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.315-324
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• 2011

In Web environment, a flood of spam causes serious social problems such as personal information leak, monetary loss from fishing and distribution of harmful contents. Moreover, types and techniques of spam distribution which must be controlled are varying as days go by. The learning based spam classification method using Bag-of-Words model is the most widely used method until now. However, this method is vulnerable to anti-spam avoidance techniques, which recent spams commonly have, because it classifies spam documents utilizing only keyword occurrence information from classification model training process. In this paper, we propose a spam document detection method using a characteristic of repeating words occurring in spam documents as a solution of anti-spam avoidance techniques. Recently, most spam documents have a trend of repeating key phrases that are designed to spread, and this trend can be used as a measure in classifying spam documents. In this paper, we define six variables, which represent a characteristic of word repetition, and use those variables as a feature set for constructing a classification model. The effectiveness of proposed method is evaluated by an experiment with blog posts and E-mail data. The result of experiment shows that the proposed method outperforms other approaches.