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…mehr
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.
Dr. Weina Niu is an associate professor and master's supervisor in the School of Computer Science and Engineering (School of Cyberspace Security), University of Electronic Science and Technology of China. He has published many papers in international academic journals/conferences on key issues in malicious traffic analysis, malicious code detection, and log anomaly detection; he has served as a reviewer for several well-known academic journals in the field of network security. He is currently the deputy director of the 5G Application Innovation Joint Laboratory of China Mobile (Chengdu) Industrial Research Institute and the Institute of Cyberspace Security of the University of Electronic Science and Technology of China. Dr. Xiaosong Zhang is a professor and doctoral supervisor in the School of Computer Science and Engineering (School of Cyberspace Security) at the University of Electronic Science and Technology of China and serves as the Dean ofthe School of Cyberspace Security. Prof. Zhang has long been devoted to basic research, technology research, and talent training in the field of information technology and security, carrying out systematic and innovative research around core issues in software and network security, big data, and blockchain security and applications. He is currently the deputy director of the Network Security Committee of the Sichuan Electronics Society and the deputy director of the Blockchain Branch of the Chinese Electronics Society. He has published many academic papers in top academic conferences and journals such as CCS, NDSS, TSE, TIFS, etc. He has published and translated several monographs related to cybersecurity. Ran Yan is currently a PhD student majoring in Cybersecurity in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). His research focus is on evasion technology of Android malware. Gong Jiacheng is currently a PhD student majoring in Cybersecurity in the School of Computer Science and Engineering at the University of Electronic Science and Technology of China (UESTC). His research focus is on intelligent detection of Android malware.
Inhaltsangabe
Chapter1: Introduction of Android malware detection.-Chapter2: Feature Code Based Android Malware Detection Method.-Chapter3: Behavior based detection method for Android malware.-Chapter4: AI-Based Android Malware Detection Methods.-Chapter5: Static adversarial method.-Chapter6: Dynamic Adversarial Method in Android.-Chapter7: AI-based Adversarial Method in Android.- Chapter8: Future Trends in Android Malware Detection
