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Titel:

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

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Elektronisches Dokument
Autor(en):
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-Dezimalklassifikation:
000 Informatik, Wissen, Systeme
Kongress- / Buchtitel:
To appear in Usenix Security, 2017
Jahr:
2017
Reviewed:
ja
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