Intelligent mobile malware detection

• Verfasst von: Thomas, Tony [VerfasserIn]
• Verfasst von: John, Teenu S. [VerfasserIn]
• Verfasst von: Surendran, Roopak [VerfasserIn]
• Verfasst von: Alazab, Mamoun [VerfasserIn]
• Titel: Intelligent mobile malware detection
• Verf.angabe: Tony Thomas, Roopak Surendran and Teenu S. John, Mamoun Alazab
• Ausgabe: First edition.
• Verlagsort: Boca Raton, FL
• Verlag: CRC Press
• Jahr: 2023
• Umfang: 1 online resource
• Gesamttitel/Reihe: Security, privacy, and trust in mobile communications
• Fussnoten: Includes bibliographical references and index. - Description based on online resource; title from digital title page (viewed on February 07, 2023)
• ISBN: 978-1-000-82497-1
• ISBN: 1-000-82497-7
• ISBN: 978-1-003-12151-0
• ISBN: 1-003-12151-9
• ISBN: 978-1-000-82498-8
• ISBN: 1-000-82498-5
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: COMPUTERS / Security / Viruses
• Sach-SW: COMPUTERS / Networking / Security
• Sach-SW: Computer crimes ; Prevention
• Sach-SW: Malware (Computer software)
• K10plus-PPN: 1846840295
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m432672529X
• Lokale URL Inst.: Zum Volltext

Abstract

The popularity of Android mobile phones has caused more cybercriminalstocreate malware applications that carry out various malicious activities. The attacks, whichescalatedafter the COVID-19 pandemic, proved there is great importance in protecting Android mobile devices from malware attacks. Intelligent Mobile Malware Detection will teach users how to develop intelligent Android malware detection mechanisms by using various graph and stochastic models. The book begins with an introduction to the Android operating system accompanied by the limitations of the state-of-the-art static malware detection mechanisms as well as a detailed presentation of a hybrid malware detection mechanism. The text then presents four different system call-based dynamic Android malware detection mechanisms using graph centrality measures, graph signal processing and graph convolutional networks. Further, the text shows how most of the Android malware can be detected by checking the presence of a unique subsequence of system calls in its system call sequence. All the malware detection mechanisms presented in the book are based on the authors' recent research. The experiments are conducted with the latest Android malware samples, andthe malware samples are collected from public repositories. The source codes are also provided for easy implementation of the mechanisms. This book will be highly useful to Android malware researchers, developers, students and cyber security professionals to explore and build defense mechanisms against the ever-evolving Android malware.