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Android Malware Detection using Machine Learning : Data-Driven Fingerprinting and Threat Intelligence /

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...

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Bibliographic Details
Main Authors: Karbab, ElMouatez Billah (Author), Debbabi, Mourad (Author), Derhab, Abdelouahid (Author), Mouheb, Djedjiga (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Advances in Information Security, 86
Subjects:
Computer networks > Security measures.
Machine learning.
Pattern recognition.
Mobile computing.
Mobile and Network Security.
Machine Learning.
Pattern Recognition.
Mobile Computing.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Introduction
  • Background and Related Work
  • Fingerprinting Android Malware Packages
  • Robust Android Malicious Community Fingerprinting
  • Android Malware Fingerprinting Using Dynamic Analysis
  • Fingerprinting Cyber-Infrastructures of Android Malware
  • Portable Supervised Malware Fingerprinting using Deep Learning
  • Resilient and Adaptive Android Malware Fingerprinting and Detection
  • Conclusion.