Intelligent Mobile Malware Detection  book cover
1st Edition

Intelligent Mobile Malware Detection

  • Available for pre-order. Item will ship after December 30, 2022
ISBN 9780367638719
December 30, 2022 Forthcoming by CRC Press
200 Pages 30 B/W Illustrations

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USD $95.00

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Book Description

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, it 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.

Table of Contents

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


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Tony Thomas is an associate professor at the Indian Institute of Information Technology and Management, Kerala (IIITM-K), India. He earned his master’s and Ph.D degrees from IIT Kanpur. After completing his PhD, he pursued postdoctoral research at the Korea Advanced Institute of Science and Technology, Daejeon, South Korea. He later worked as a member of the research staff at the General Motors Research Lab, Bangalore, India, and the School of Computer Engineering, Nanyang Technological University, Singapore. His current research interests include malware analysis, biometrics, cryptography, machine learning, cyber threat prediction and visualization, digital watermarking, multimedia security and digital forensics.

Roopak Surendran is currently pursuing his PhD in the area of Android malware analysis at the Indian Institute of Information Technology and Management-Kerala (IIITM-K). Before joining his PhD program, he completed his MPhil degree in computer science with a specialization in cyber security from IIITM-K. He has published several research papers related to Android malware analysis and phishing detection. His research interests include malware analysis and phishing detection.

Teenu S. John holds an MTech degree in computer science with specialization in data security from TocH Institute of Science and Technology, part of the Cochin University of Science and Technology, Kerala, India and a BTech degree in Information Technology from the College of Engineering Perumon, also part of the Cochin University of Science and Technology, Kerala, India. She is currently doing her PhD in detecting adversarial attacks in Android malware detection at the Indian Institute of Information Technology and Management, Kerala (IIITM-K). Her research interests include malware analysis, machine learning for cybersecurity, data analytics and cyber threat detection.

Mamoun Alazab is an associate professor at the College of Engineering, IT and Environment at Charles Darwin University, Australia. Dr. Alazab’s research is multidisciplinary and focuses on cybersecurity, which includes current and emerging issues in cyber environments such as cyber-physical systems, specifically cybercrime detection and prevention. His research focuses on human behavior, computational analysis like AI, machine learning, including deep learning for access control and biometrics, and human information processing concerns in security and privacy. He has published more than 150 research papers in many international journals and conferences. His research over the years has contributed to the development of several successful secure commercial systems in the areas of secure network systems, security tools, AI security, secure mobile systems, as well as cryptographic, cyber-physical systems for security, and applications where the devices are often lightweight.