Malicious content filtering based on semantic features

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.