A Semantic-Based Malware Detection System Design Based on Channels

Abstract : With the development of information technology, there are massive and heterogeneous data resources in the internet, as well as the malwares are appearing in different forms, traditional text-based malware detection cannot efficiently detect the various malwares. So it is becoming a great challenge about how to realize semantic-based malware detection. This paper proposes an intelligent and active data interactive coordination model based on channels. The coordination channels are the basic construction unit of this model, which can realize various data transmissions. By defining the coordination channels, the coordination atoms and the coordination units, the model can support diverse data interactions and can understand the semantic of different data resources. Moreover, the model supports graphical representation of data interaction, so we can design complex data interaction system in the forms of flow graph. Finally, we design a semantic-based malware detection system using our model; the system can understand the behavior semantics of different malwares, realizing the intelligent and active malware detection.
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Peige Ren, Xiaofeng Wang, Chunqing Wu, Baokang Zhao, Hao Sun. A Semantic-Based Malware Detection System Design Based on Channels. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.653-662, ⟨10.1007/978-3-642-55032-4_67⟩. ⟨hal-01397283⟩

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