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About Malware Detection in Android App Using Static and Dynamic Analysis
This project report describes developing a system called DroidDetector to detect malware in Android apps using static and dynamic analysis with deep learning techniques. The system analyzes features from static analysis of app code and dynamic analysis of app behavior. These features are then used to train a deep learning model to classify apps as malware or benign. The results show DroidDetector achieves 96.76% detection accuracy, outperforming traditional machine learning methods. An evaluation of 10 antivirus tools also demonstrates the potential to improve Android malware detection.
Detailed Information
| Author: | ['shital'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 165 |
| Language: | English |
| Format: | |
| Price: | FREE |
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