Android malware detection using machine learning : data-driven fingerprinting and threat intelligence / ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb.
2021
QA76.76.C68
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Android malware detection using machine learning : data-driven fingerprinting and threat intelligence / ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb.
ISBN
9783030746643 (electronic bk.)
303074664X (electronic bk.)
3030746631
9783030746636
303074664X (electronic bk.)
3030746631
9783030746636
Publication Details
Cham, Switzerland : Springer, 2021.
Language
English
Description
1 online resource
Item Number
10.1007/978-3-030-74664-3 doi
Call Number
QA76.76.C68
Dewey Decimal Classification
005.8/8
Summary
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 applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 21, 2021).
Series
Advances in information security ; 86. 1568-2633
Available in Other Form
Android malware detection using machine learning.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
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.
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.