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About Droiddeep: Using Deep Belief Network To Characterize and Detect Android Malware
This paper proposes DroidDeep, a method using Deep Belief Networks to characterize and detect Android malware through static behavioral analysis. It extracts 11 types of static behavioral characteristics from Android apps and uses a Deep Belief Network model to select key characteristics and classify apps as benign or malicious. The method was able to accurately detect 99.4% of malware in a test dataset containing both benign and malicious Android apps while analyzing each app in about 6 seconds on average.
Detailed Information
| Author: | ['Elie El Rif'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 14 |
| Language: | English |
| Format: | |
| Price: | FREE |
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