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Dokumenttyp:
Konferenzbeitrag 
Art des Konferenzbeitrags:
Elektronisches Dokument 
Autor(en):
Banescu, Sebastian and Collberg, Christian and Pretschner, Alexander 
Titel:
Predicting the Resilience of Obfuscated Code Against Symbolic Execution Attacks via Machine Learning 
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 auto...    »
 
Dewey-Dezimalklassifikation:
000 Informatik, Wissen, Systeme 
Kongress- / Buchtitel:
To appear in Usenix Security, 2017 
Jahr:
2017 
Reviewed:
ja