Monet: A User-oriented Behavior-based Malware Variants Detection System for Android


Android, the most prominent portable OS, has around 78% of the versatile piece of the overall industry. Because of its prevalence, it draws in numerous malware assaults. Indeed, individuals have found around 1 million new malware tests for each quarter, and it was accounted for that more than 98% of these new malware tests are in actuality “subsidiaries” (or variations) from existing malware families.


In this project, we first demonstrate that runtime practices of malware’s center functionalities are in reality comparative inside a malware family. Subsequently, we propose a system to consolidate “runtime conduct” with “static structures” to identify malware variations. We present the outline and execution of Monet, which has a customer and a backend server module. The customer module is a lightweight, in-gadget application for conduct checking and signature age, and we understand this utilizing two novel capture attempt methods. The backend server is in charge of substantial scale malware recognition. We gather 3723 malware tests and best 500 considerate applications to do broad analyses of recognizing malware variations and shielding against malware change.

Our investigations demonstrate that Monet can accomplish around 99% exactness in distinguishing malware variations. Besides, it can protect against ten distinctive muddling and change systems, while just acquires around 7% execution overhead and around 3% battery overhead. All the more critically, Monet will consequently alarm clients with interruption points of interest so to anticipate advance vindictive practices.


CPU type : Intel Pentium 4

Clock speed : 3.0 GHz

Ram size : 512 MB

Hard disk capacity : 40 GB

Monitor type : 15 Inch shading screen

Keyboard type : web console



Working System: Android Studio

Language : ANDROID SDK 7.0

Documentation : Ms-Office




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