The Android Malware Handbook

Informationen zum Autor Qian Han, Salvador Mandujano, Sebastian Porst, V.S. Subrahmanian, Sai Deep Tetal i, and Yanhai Xiong Klappentext "Explores the history of Android attacks and covers static and dynamic approaches to analyzing real malware specimens, machine-learning techniques to detect malicious apps, and how to identify banking trojans, ransomware, and SMS fraud"-- Zusammenfassung 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 groundbreaking guide to Android malware distills years of research by machine learning experts in academia and members of Meta and Google's Android Security teams into a comprehensive introduction to detecting common threats facing the Android eco-system today. Explore the history of Android malware in the wild since the operating system first launched and then practice static and dynamic approaches to analyzing real malware specimens. Next, examine machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input to these models. Adapt these machine learning strategies to the identifica-tion of malware categories like banking trojans, ransomware, and SMS fraud. You'll: Dive deep into the source code of real malware Explore the static, dynamic, and complex features you can extract from malware for analysis Master the machine learning algorithms useful for malware detection Survey the efficacy of machine learning techniques at detecting common Android malware categories The Android Malware Handbook 's team of expert authors will guide you through the Android threat landscape and prepare you for the next wave of malware to come. Inhaltsverzeichnis 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...

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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 groundbreaking guide to Android malware distills years of research by machine learning experts in academia and members of Meta and Google’s Android Security teams into a comprehensive introduction to detecting common threats facing the Android eco-system today.

Explore the history of Android malware in the wild since the operating system first launched and then practice static and dynamic approaches to analyzing real malware specimens. Next, examine machine learning techniques that can be used to detect malicious apps, the types of classification models that defenders can implement to achieve these detections, and the various malware features that can be used as input to these models. Adapt these machine learning strategies to the identifica-tion of malware categories like banking trojans, ransomware, and SMS fraud.

You’ll:

• Dive deep into the source code of real malware
• Explore the static, dynamic, and complex features you can extract from malware for analysis
• Master the machine learning algorithms useful for malware detection
• Survey the efficacy of machine learning techniques at detecting common Android malware categories
  The Android Malware Handbook’s team of expert authors will guide you through the Android threat landscape and prepare you for the next wave of malware to come.

Contenu
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