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Title:

Predicting the Resilience of Obfuscated Code Against Symbolic Execution Attacks via Machine Learning

Document type:
Konferenzbeitrag
Contribution type:
Elektronisches Dokument
Author(s):
Banescu, Sebastian and Collberg, Christian and Pretschner, Alexander
Abstract:
Software obfuscation transforms code such that it is more difficult to reverse engineer. However, it is known that given enough resources, an attacker will successfully reverse engineer an obfuscated program. Therefore, an open challenge for software obfuscation is estimating the time an obfuscated program is able to withstand a given reverse engineering attack. This paper proposes a general framework for choosing the most relevant software features to estimate the effort of automated at...     »
Dewey Decimal Classification:
000 Informatik, Wissen, Systeme
Book / Congress title:
To appear in Usenix Security, 2017
Year:
2017
Reviewed:
ja
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