Countering Android Malware: A Scalable Semi-Supervised Approach for Family-Signature Generation

Countering Android Malware: A Scalable Semi-Supervised Approach for Family-Signature Generation

Reducing the effort required by humans in countering malware is of utmost practical value. We describe a scalable, semi-supervised framework to dig into massive data sets of Android applications and identify new malware families. Until 2010, the industrial standard for the detection of malicious app...

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Bibliographic Details
Main Authors: Andrea Atzeni, Fernando Diaz, Andrea Marcelli, Antonio Sanchez, Giovanni Squillero, Alberto Tonda
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Semi-supervised learning
clustering
android
malware
automatic signature generation
Online Access:https://ieeexplore.ieee.org/document/8485352/

Internet

https://ieeexplore.ieee.org/document/8485352/

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