Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-68063-7_13 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Software protection,Code obfuscation,Potency,Neural networks | Data mining,Computer science,Decision support system,Potency,Software,Artificial intelligence,Obfuscation (software),Artificial neural network,Obfuscation,Code (cryptography),Software obfuscation,Machine learning | Conference |
Volume | ISSN | Citations |
10547 | 0302-9743 | 2 |
PageRank | References | Authors |
0.35 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Canavese | 1 | 18 | 4.03 |
| Leonardo Regano | 2 | 11 | 2.17 |
| Cataldo Basile | 3 | 114 | 14.90 |
| Alessio Viticchié | 4 | 12 | 1.84 |