Niu, Weina.
Android Malware Detection and Adversarial Methods [electronic resource] / by Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong. - 1st ed. 2024. - XIV, 190 p. 5 illus. online resource.
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.
9789819714599
10.1007/978-981-97-1459-9 doi
Computer networks--Security measures.
Data protection.
Data protection--Law and legislation.
Machine learning.
Blockchains (Databases).
Mobile and Network Security.
Data and Information Security.
Security Services.
Privacy.
Machine Learning.
Blockchain.
TK5105.59
005.8
Android Malware Detection and Adversarial Methods [electronic resource] / by Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong. - 1st ed. 2024. - XIV, 190 p. 5 illus. online resource.
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.
9789819714599
10.1007/978-981-97-1459-9 doi
Computer networks--Security measures.
Data protection.
Data protection--Law and legislation.
Machine learning.
Blockchains (Databases).
Mobile and Network Security.
Data and Information Security.
Security Services.
Privacy.
Machine Learning.
Blockchain.
TK5105.59
005.8