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Android Malware Detection and Adversarial Methods

By: Jiacheng Gong (Author) , Ran Yan (Author) , Weina Niu (Author) , Xiaosong Zhang (Author)

Extended Catalogue

Ksh 31,900.00

Format: Hardback or Cased Book

ISBN-10: 9819714583

ISBN-13: 9789819714582

Edition statement: 2024 ed.

Publisher: Springer Verlag, Singapore

Imprint: Springer Nature

Country of Manufacture: GB

Country of Publication: GB

Publication Date: May 24th, 2024

Publication Status: Active

Product extent: 190 Pages

Product Classification / Subject(s): Computer viruses, Trojans & worms
Spyware
Network security

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It is organized into four parts: the overview of Android malware detection, the general Android malware detection method, the adversarial method for Android malware detection, and the future trends of Android malware detection.

The rise of Android malware poses a significant threat to users’ information security and privacy. Malicious software can inflict severe harm on users by employing various tactics, including deception, personal information theft, and device control. To address this issue, both academia and industry are continually engaged in research and development efforts focused on detecting and countering Android malware.

This book is a comprehensive academic monograph crafted against this backdrop. The publication meticulously explores the background, methods, adversarial approaches, and future trends related to Android malware. It is organized into four parts: the overview of Android malware detection, the general Android malware detection method, the adversarial method for Android malware detection, and the future trends of Android malware detection. Within these sections, the book elucidates associated issues, principles, and highlights notable research.

By engaging with this book, readers will gain not only a global perspective on Android malware detection and adversarial methods but also a detailed understanding of the taxonomy and general methods outlined in each part. The publication illustrates both the overarching model and representative academic work, facilitating a profound comprehension of Android malware detection.



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