The Android Malware Handbook: Using Manual Analysis and ML-Based Detection
The Android Malware Handbook: Using Manual Analysis and ML-Based Detection
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
Descrierea produsului
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
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