The Android Malware Handbook: Detection and Analysis by Human and MachineNo Starch Press, 2023年11月7日 - 328页 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:
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. |
目录
Collaboration Across Google | 9 |
Up Next | 26 |
3 | 71 |
Analyzing the BIND_NOTIFICATION_LISTENER_SERVICE Permission Malicious Code in App Entry Points | 78 |
The Malwares First Stage | 86 |
The Malwares Third Stage | 94 |
27 | 105 |
Up Next | 112 |
Correlation GraphBased Feature | 201 |
Rooting Malware vs Goodware | 207 |
Rooting Malware vs Other Malware | 214 |
Spyware | 220 |
A Case Study | 227 |
Up Next | 233 |
Banking Trojans vs Other Malware | 242 |
A Case Study | 246 |
Resetting the Emulator | 118 |
Analyzing Network Traffic | 125 |
Decrypting the CommandandControl Communications | 131 |
CommandandControl Server Messages | 138 |
Adding Static Analysis | 145 |
Up Next | 157 |
Classification Algorithms | 167 |
Evaluating Machine Learning Models | 174 |
Machine Learning Fundamentals Chapter 6 Machine Learning | 181 |
Triadic Suspicion Graph Features | 187 |
LandmarkBased Features | 195 |
How Ransomware Attacks Work | 252 |
A Case | 261 |
11 | 267 |
SMS Fraud vs Other Malware | 275 |
12 | 283 |
Distribution | 289 |
Malware Economics | 291 |
| 295 | |
| 296 | |
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常见术语和短语
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