Written by machine-learning researchers and members of the Android Security team, this all-star guide tackles the analysis and detection of malware that targets the Android operating system.

This comprehensive guide to Android malware introduces current threats facing the world’s most widely used operating system. After exploring the history of attacks seen in the wild since the time Android first launched, including several malware families previously absent from the literature, you’ll practice static and dynamic approaches to analyzing real malware specimens. Next, you’ll examine the machine-learning techniques used to detect malicious apps, the types of classification models that defenders can use, and the various features of malware specimens that can become input to these models. You’ll then adapt these machine-learning strategies to the identification of malware categories like banking trojans, ransomware, and SMS fraud.

You’ll learn: 

• How historical Android malware can elevate your understanding of current threats 
  
• How to manually identify and analyze current Android malware using static and dynamic reverse-engineering tools
  
• How machine-learning algorithms can analyze thousands of apps to detect malware at scale
  

Qian Han, Research Scientist at Meta since 2021, received his PhD in Computer Science from Dartmouth College and  his Bachelor’s in Electronic Engineering from Tsinghua University, Beijing, China.

Sai Deep Tetali, Principal Engineer and Tech Lead Manager at Meta, works on privacy solutions for augmented and virtual reality applications. He spent 5 years at Google developing machine learning techniques to detect Android malware and has a PhD from University of California Los Angeles.

Salvador Mandujano, Security Engineering Manager at Google, has led product security engineering, malware reverse engineering and payments security teams. Before Google, he held senior security research and architecture positions at Intel and Nvidia. He has a PhD in Artificial Intelligence from Tecnológico de Monterrey, an MSc in Computer Science from Purdue, an MBA from The University of Texas, and a BSc in Computer Engineering from Universidad Nacional Autónoma de México.

Sebastian Porst is manager of Google’s Android Application Security Research team, which tries to predict or research novel attacks on Android devices and Android users by malware or through app vulnerabilities. He has an MSc Masters from Trier University of Applied Sciences, Germany in 2007.

V.S. Subrahmanian is the Walter P. Murphy Professor of Computer Science and Buffet Faculty Fellow in the Buffet Institute of Global Affairs at Northwestern University. Prof. Subrahmanian is one of the world’s foremost experts at the intersection of AI and security issues. He has written eight books, edited ten, and published over 300 refereed articles.

Yanhai Xiong is currently an Assistant Professor in the Department of Computer Science and Engineering at the University of Louisville. She has a PhD from, Nanyang Technological University focusing on applying AI techniques to improve the efficiency of electric vehicle infrastructure and a BS in Engineering from the University of Science and Technology of China.

Foreword
Introduction
Part 1: A Primer on Android Malware
Chapter 1: Introduction to Android Security
Chapter 2: Android Malware in the Wild
Part 2: Manual Analysis
Chapter 3: Static Analysis
Chapter 4: Dynamic Analysis
Part 3: Machine Learning Detection
Chapter 5: Machine Learning Fundamentals
Chapter 6: Machine Learning Features
Chapter 7: Rooting Malware
Chapter 8: Spyware
Chapter 9: Banking Trojans
Chapter 10: Ransomware
Chapter 11: SMS Fraud
Chapter 12: The Future of Android Malware
Index