Intelligent Mobile Malware Detection

This book provides essential strategies for developing intelligent detection mechanisms against Android malware, offering valuable insights for researchers and cybersecurity professionals. Intelligent Mobile Malware Detection is a must-read.

The rise in popularity of Android mobile phones has led to an increase in cybercriminal activities, particularly the development of malware applications designed to execute various harmful actions. Intelligent Mobile Malware Detection serves as a critical resource for Android malware researchers, developers, students, and cybersecurity professionals, offering insights into how to create effective defense mechanisms against these threats. As attacks escalated following the COVID-19 pandemic, the need for robust protection for Android devices has become more apparent than ever.

This book delves into the intricacies of Android operating systems and highlights the limitations of existing static malware detection methods. It introduces readers to a hybrid malware detection mechanism that combines various approaches to enhance detection capabilities. Additionally, the text explores four distinct system call-based dynamic malware detection mechanisms, utilizing innovative techniques such as graph centrality measures, graph signal processing, and graph convolutional networks.

One of the key takeaways from Intelligent Mobile Malware Detection is the identification of unique subsequences of system calls, which can be critical in detecting most Android malware. The authors present their recent research findings, supported by experiments conducted with the latest malware samples collected from public repositories. With source codes provided for practical implementation, this book is an invaluable tool for anyone looking to deepen their understanding of Android malware and develop effective detection strategies.