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Mobile security
The document proposes a methodology called Mobsafe for evaluating mobile apps using cloud computing and data mining to filter out malware. It outlines a hybrid approach combining static and dynamic analysis for enhanced security, utilizing a cloud infrastructure with tools for automated analysis. Future enhancements may include web mining and machine learning techniques for improved malware detection.
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Introduction of Mobsafe by Sowmya Bk from VTU, focusing on cloud computing and data mining in mobile apps.
Increase in mobile threats; methodology proposed to evaluate apps using cloud and data mining techniques.
Current systems use permission-based and semantics-aware techniques for malware detection.
New methodology combining static and dynamic methods with data mining to enhance mobile security.
Mobsafe operates on a cloud platform (CloudStack) with tools for Android app analysis, detailing its working principles.
Mobsafe includes a web frontend and tools for app analysis, aiming to implement machine learning for optimized outputs.
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Mobile security
- 1. Mobsafe: Using Cloudcomputing and Data mining Name: sowmya Bk Email id: sowmya.harshini@gmail.com University: VTU Sem: 2nd Branch : software engg (M.tech)
- 2. Introduction ď With theexplosive increase in mobile apps,more and more threats Migrate from traditional PC to mobile devices. ď we propose a methodology to evaluate mobile apps based on cloud computing and data mining. ď Our evaluation results show that its practical use of cloud platform and data mining to verify all stored apps routenely and filter out malware apps from mobile app markets
- 3. ď Mobile Threats. ďSome root causes for android malware origins. ď some known malwares in android platform. ďStatic and dynamic behavior analysis.
- 4. Existing system ď Permissionbased method was using for identifying malwares. ď Semantics-Aware based technique was used.
- 5. Proposed System ď Inthis work a methodology is being proposed which provides security to your mobile phone from malwares . ď It includes combination of dynamic and static methods to provide security. ď It includes data mining techniques. ď It includes some method that is used to provide optimized outputs.
- 6. Mobsafe ď It hasinfrastructure cloud platfarm ďź It has cloud stack(home-brewed platform). Hadoop storage for mobile apps. ď Work principle ďź Mobsafe is a system to check whether an android app is virulence based on some tools in cloud platfarm it will verify the malware. ďź Mobsafe is an automatize system which can be used to analyze android apps.
- 7. Infrastructure cloud platformbased on CloudStack. Architecture
- 8. The procedure ofAndroid analysis in Mobsafe.
- 9. ď Mobsafe hasa web frontend. ď Mobsafe has two backend tools , They are ASEF and SAAF. ď ASEF is an automates tool which can be used to analyze Android apps by using ADB and AVD. ď SAAF is static analyzer for android apk files.
- 10. Interest Area ďAs thefuture perspective some web mining and more advanced data mining techniques will be implemented to get more optimized outputs. ďI would like to implement Machine learning is a promising technology to identify mobile apps virulence based on data mining. ď If I get internship I would like to work on mobile application domain and Testing/