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The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities. The attacks, which escalated after 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…mehr
The popularity of Android mobile phones has caused more cybercriminals to create malware applications that carry out various malicious activities. The attacks, which escalated after 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, and the 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.
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Dr. Tony Thomas is currently associate professor in the School of Computer Science and Engineering, Kerala University of Digital Sciences, Innovation and Technology, India (formerly IIITM-K). He completed his master's and PhD degrees from IIT Kanpur. After completing his PhD, he carried out his post-doctoral research at the Korea Advanced Institute of Science and Technology. After that, he joined as a researcher at the General Motors Research Lab, Bangalore, India. He later moved to the School of Computer Engineering, Nanyang Technological University, Singapore as a research fellow. In 2011, he joined as an assistant professor at Indian Institute of Information Technology and Management-Kerala (IIITM-K). He is an associate editor and reviewer of several journals. He is a member of the Board of Studies of several universities. His current research interests include: malware analysis, biometrics, cryptography, quantum computation and machine learning applications in cyber security. He has published many research papers, book chapters and books in these domains. He is an author of the book Machine Learning Approaches in Cyber Security Analytics published by Springer.
Dr. Roopak Surendran is currently working as a penetration tester at the Kerala Security Audit and Assurance Centre (K-SAAC) of the Kerala University of Digital Sciences Innovation and Technology. He has done his PhD research in Android malware analysis, which was funded by the Kerala state planning board. Before joining the PhD program, he completed his MPhil degree in computer science with a specialization in cyber security from Indian Institute of Information Technology and Management-Kerala. He published many research papers related to malware analysis and phishing detection. Also, he has developed Python-based tools and sandboxes to protect devices from phishing and malware attacks. His interests include: web application security, mobile application security, malware analysis and phishing detection.
Ms. Teenu S. John holds an MTech degree in computer science with specialization in data security from TocH Institute of Science and Technology under Cochin University of Science and Technology, Kerala, India, and a BTech degree in information technology from the College of Engineering Perumon, under Cochin University of Science and Technology-Kerala, India. She is currently doing her PhD on adversarial malware detection at the Kerala University of Digital Sciences Innovation and Technology, formerly Indian Institute of Information Technology and Management-Kerala (IIITM-K). Her research interests include: malware analysis, machine learning for cyber security, data analytics and cyber threat detection.
Dr. Mamoun Alazab is associate professor at the College of Engineering, IT and Environment, and is the director of the NT Academic Centre for Cyber Security and Innovation (ACCI) at Charles Darwin University, Australia. He received his PhD in computer science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cyber security researcher and practitioner with industry and academic experience. Dr. Alazab's research is multidisciplinary focusing on cyber security including current and emerging issues in the cyber environment like cyber-physical systems and Internet of Things, with a focus on cybercrime detection and prevention. He has more than 300 research papers, 11 authored and edited books, as well as 3 patents. As of March 2022, 9256 citations appear on Google. His research over the years has contributed to the development of several successful secure commercial systems. His book, Malware Analysis Using Artificial Intelligence and Deep Learning, reached 40k downloads in about 1 year and was referred to by Microsoft research and Google research. He is the recipient of several prestigious awards including the NT Young Tall Poppy of the Year (2021) from the Australian Institute of Policy and Science (AIPS) and the Japan Society for the Promotion of Science (JSPS) fellowship through the Australian Academy of Science. He worked previously as a senior lecturer (Australian National University) and lecturer (Macquarie University). He is a senior member of the IEEE, and the founding chair of the IEEE Northern Territory (NT) Subsection. He serves as the associate editor of IEEE Transactions on Computational Social Systems, IEEE Transactions on Network and Service Management (TNSM), ACM Digital Threats: Research and Practice, and Complex & Intelligent Systems.
Inhaltsangabe
1. Internet and Android OS
2. Android Malware
3. Static Malware Detection
4. Dynamic and Hybrid Malware Detection
5. Detection Using Graph Centrality Measures
6. Graph Convolutional Network for Detection
7. Graph Signal Processing Based Detection
8. System Call Pattern Based Detection
9. Conclusions and Future Directions
Index
1. Internet and Android OS 2. Android Malware 3. Static Malware Detection 4. Dynamic and Hybrid Malware Detection 5. Detection Using Graph Centrality Measures 6. Graph Convolutional Network for Detection 7. Graph Signal Processing Based Detection 8. System Call Pattern Based Detection 9. Conclusions and Future Directions Index
1. Internet and Android OS 2. Android Malware 3. Static Malware Detection 4. Dynamic and Hybrid Malware Detection 5. Detection Using Graph Centrality Measures 6. Graph Convolutional Network for Detection 7. Graph Signal Processing Based Detection 8. System Call Pattern Based Detection 9. Conclusions and Future Directions Index
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