Estimating Software Obfuscation Potency with Artificial Neural Networks

REGANO, LEONARDO;
2017-01-01

Abstract

This paper presents an approach to estimate the potency of obfuscation techniques. Our approach uses neural networks to accurately predict the value of complexity metrics – which are used to compute the potency – after an obfuscation transformation is applied to a code region. This work is the first step towards a decision support to optimally protect software applications.
2017
Inglese
STM - 2017: 13th International Workshop on Security and Trust Management
978-3-319-68062-0
Springer International Publishing
New York City
STATI UNITI D'AMERICA
10547
193
202
10
WOS:000463256600013
2-s2.0-85030158046
https://link.springer.com/chapter/10.1007/978-3-319-68063-7_13
STM - 2017: 13th International Workshop on Security and Trust Management
Esperti anonimi
September 14–15, 2017
Oslo (NO)
scientifica
Software protection
Code obfuscation
Potency
Neural networks
no
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Canavese, Daniele; Regano, Leonardo; Basile, Cataldo; Viticchie', Alessio
273
4
4.1 Contributo in Atti di convegno
none
info:eu-repo/semantics/conferencePaper
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