%0 Conference Proceedings
%T DiffSig: Resource Differentiation Based Malware Behavioral Concise Signature Generation
%+ National University of Defense Technology
%A Lu, Huabiao
%A Zhao, Baokang
%A Wang, Xiaofeng
%A Su, Jinshu
%Z Part 2: Asian Conference on Availability, Reliability and Security (AsiaARES)
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Conference on Information and Communication Technology (ICT-EurAsia)
%C Yogyakarta, Indonesia
%Y David Hutchison
%Y Takeo Kanade
%Y Madhu Sudan
%Y Demetri Terzopoulos
%Y Doug Tygar
%Y Moshe Y. Vardi
%Y Gerhard Weikum
%Y Khabib Mustofa
%Y Erich J. Neuhold
%Y A Min Tjoa
%Y Edgar Weippl
%Y Ilsun You
%Y Josef Kittler
%Y Jon M. Kleinberg
%Y Friedemann Mattern
%Y John C. Mitchell
%Y Moni Naor
%Y Oscar Nierstrasz
%Y C. Pandu Rangan
%Y Bernhard Steffen
%I Springer
%3 Information and Communicatiaon Technology
%V LNCS-7804
%P 271-284
%8 2013-03-25
%D 2013
%R 10.1007/978-3-642-36818-9_28
%K Behavioral Signature
%K Anti-obfuscation
%K Scalable
%K Resource Differentiation
%K Iterative Sequence Alignment
%K Handle Dependency
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Malware obfuscation obscures malware into a different form that’s functionally identical to the original one, and makes syntactic signature ineffective. Furthermore, malware samples are huge and growing at an exponential pace. Behavioral signature is an effective way to defeat obfuscation. However, state-of-the-art behavioral signature, behavior graph, is although very effective but unfortunately too complicated and not scalable to handle exponential growing malware samples; in addition, it is too slow to be used as real-time detectors. This paper proposes an anti-obfuscation and scalable behavioral signature generation system, DiffSig, which voids information-flow tracking which is the chief culprit for the complex and inefficiency of graph behavior, thus, losing some data dependencies, but describes handle dependencies more accurate than graph behavior by restrict the profile type of resource that each handle dependency can reference to. Our experiment results show that DiffSig is scalable and efficient, and can detect new malware samples effectively.
%G English
%Z TC 5
%Z TC 8
%2 https://inria.hal.science/hal-01480181/document
%2 https://inria.hal.science/hal-01480181/file/978-3-642-36818-9_28_Chapter.pdf
%L hal-01480181
%U https://inria.hal.science/hal-01480181
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-TC8
%~ IFIP-ICT-EURASIA
%~ IFIP-LNCS-7804