Malicious content filtering based on semantic features

Authors:
Semin Kim;Hyunseok Min;Jaehyun Jeon;Yong Man Ro;Seungwan Han
Affiliations:
Korea Advanced Institute of Science and Technology, Yuseong-gu, Republic of Korea;Korea Advanced Institute of Science and Technology, Yuseong-gu, Republic of Korea;Korea Advanced Institute of Science and Technology, Yuseong-gu, Republic of Korea;Korea Advanced Institute of Science and Technology, Yuseong-gu, Republic of Korea;Electronics and Telecommunications Research Institute, Yuseong-gu, Republic of Korea
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

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Abstract

This paper proposes a method to filtering malicious contents using semantic features. In conventional content based approach, low-level features such as color and texture are used to filter malicious contents. But, it is difficult to detect them because of semantic gaps between the low-level features and global concepts. In this paper, global concepts are divided into several semantic features. These semantic features are used to classify the global concept of malicious contents. We design semantic features and construct semantic classifier. In experiment, we evaluate the performance to filter malicious contents by comparing low-level features and semantic features. Results show that our proposed method has better performance than the method using only low-level features.