
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures.
First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applica ...
DETAILS
Android Malware Detection using Machine Learning
Data-Driven Fingerprinting and Threat Intelligence
Karbab, ElMouatez Billah, Debbabi, Mourad, Derhab, Abdelouahid
Kartoniert, xiv, 202 S.
XIV, 202 p. 81 illus., 64 illus. in color.
Sprache: Englisch
235 mm
ISBN-13: 978-3-030-74666-7
Titelnr.: 95980222
Gewicht: 338 g
Springer, Berlin (2022)
Herstelleradresse
Springer Heidelberg
Tiergartenstr. 17
69121 - DE Heidelberg
E-Mail: buchhandel-buch@springer.com