The Android Malware Handbook
Using Manual Analysis and ML-Based Detection
Published on 7. November 2023
Book
Paperback/Softback
328 pages
978-1-7185-0330-4 (ISBN)
Description
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 analysing 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 analyse current Android malware using static and dynamic reverse-engineering tools; How machine-learning algorithms can analyse thousands of apps to detect malware at scale.
Reviews / Votes
"A comprehensive introduction to Android malware and its analysis."-Maik Morgenstern, CTO at AV-TEST
"An indispensable resource for both security professionals and enthusiasts, offering unparalleled insights into the intricacies of Android malware and empowering readers to effectively guard against this pervasive threat."
-Dimitrios Valsamaras, Senior Security Researcher at Microsoft (formerly worked on Android at Google)
"Comprehensive and exceptionally user friendly, The Android Malware Handbook should be considered essential reading for anyone with an interest in computer viruses, computer software testing, and computer hacking."
-Midwest Book Review
More details
Language
English
Place of publication
San Francisco
United States
Product notice
Paperback (trade)
Dimensions
Height: 235 mm
Width: 177 mm
Thickness: 23 mm
Weight
654 gr
ISBN-13
978-1-7185-0330-4 (9781718503304)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Qian Han | Salvador Mandujano | Sebastian Porst
The Android Malware Handbook
Detection and Analysis by Human and Machine
E-Book
11/2023
No Starch Press
€38.99
Available for download
Persons
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.
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 Tecnologico 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 Autonoma de Mexico.
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.
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.
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.
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 Tecnologico 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 Autonoma de Mexico.
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.
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.
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.
Content
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
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