%0 Conference Proceedings
%T A Semantic-Based Malware Detection System Design Based on Channels
%+ National University of Defense Technology [China]
%A Ren, Peige
%A Wang, Xiaofeng
%A Wu, Chunqing
%A Zhao, Baokang
%A Sun, Hao
%Z Part 2: The 2014 Asian Conference on Availability, Reliability and Security, AsiaARES 2014
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia)
%C Bali, Indonesia
%Y David Hutchison
%Y Takeo Kanade
%Y Bernhard Steffen
%Y Demetri Terzopoulos
%Y Doug Tygar
%Y Gerhard Weikum
%Y Linawati
%Y Made Sudiana Mahendra
%Y Erich J. Neuhold
%Y A Min Tjoa
%Y Ilsun You
%Y Josef Kittler
%Y Jon M. Kleinberg
%Y Alfred Kobsa
%Y Friedemann Mattern
%Y John C. Mitchell
%Y Moni Naor
%Y Oscar Nierstrasz
%Y C. Pandu Rangan
%I Springer
%3 Information and Communication Technology
%V LNCS-8407
%P 653-662
%8 2014-04-14
%D 2014
%R 10.1007/978-3-642-55032-4_67
%K Malware detection
%K interactive coordination model
%K semantic
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 5
%Z TC 8
%2 https://inria.hal.science/hal-01397283/document
%2 https://inria.hal.science/hal-01397283/file/978-3-642-55032-4_67_Chapter.pdf
%L hal-01397283
%U https://inria.hal.science/hal-01397283
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-TC8
%~ IFIP-ICT-EURASIA
%~ IFIP-LNCS-8407