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Mobile security

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Mphasis
Mphasis

In this presentation, Sowmya presents an interesting application that finds malware/viruses in mobile platforms through the use of data mining techniques

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Mobsafe: Using Cloud computing and Data mining Name: sowmya Bk Email id: sowmya.harshini@gmail.com University: VTU Sem: 2nd Branch : software engg (M.tech)
Introduction  With the explosive increase in mobile apps,more and more threats Migrate from traditional PC to mobile devices.  we propose a methodology to evaluate mobile apps based on cloud computing and data mining.  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
 Mobile Threats.  Some root causes for android malware origins.  some known malwares in android platform. Static and dynamic behavior analysis.
Existing system  Permission based method was using for identifying malwares.  Semantics-Aware based technique was used.
Proposed System  In this work a methodology is being proposed which provides security to your mobile phone from malwares .  It includes combination of dynamic and static methods to provide security.  It includes data mining techniques.  It includes some method that is used to provide optimized outputs.
Mobsafe  It has infrastructure cloud platfarm  It has cloud stack(home-brewed platform). Hadoop storage for mobile apps.  Work principle  Mobsafe is a system to check whether an android app is virulence based on some tools in cloud platfarm it will verify the malware.  Mobsafe is an automatize system which can be used to analyze android apps.
Infrastructure cloud platform based on CloudStack. Architecture
The procedure of Android analysis in Mobsafe.
 Mobsafe has a web frontend.  Mobsafe has two backend tools , They are ASEF and SAAF.  ASEF is an automates tool which can be used to analyze Android apps by using ADB and AVD.  SAAF is static analyzer for android apk files.
Interest Area As the future perspective some web mining and more advanced data mining techniques will be implemented to get more optimized outputs. I would like to implement Machine learning is a promising technology to identify mobile apps virulence based on data mining.  If I get internship I would like to work on mobile application domain and Testing/
Mobile security

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Mobile security

  • 1. Mobsafe: Using Cloud computing and Data mining Name: sowmya Bk Email id: sowmya.harshini@gmail.com University: VTU Sem: 2nd Branch : software engg (M.tech)
  • 2. Introduction  With the explosive increase in mobile apps,more and more threats Migrate from traditional PC to mobile devices.  we propose a methodology to evaluate mobile apps based on cloud computing and data mining.  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.  Mobile Threats.  Some root causes for android malware origins.  some known malwares in android platform. Static and dynamic behavior analysis.
  • 4. Existing system  Permission based method was using for identifying malwares.  Semantics-Aware based technique was used.
  • 5. Proposed System  In this work a methodology is being proposed which provides security to your mobile phone from malwares .  It includes combination of dynamic and static methods to provide security.  It includes data mining techniques.  It includes some method that is used to provide optimized outputs.
  • 6. Mobsafe  It has infrastructure cloud platfarm  It has cloud stack(home-brewed platform). Hadoop storage for mobile apps.  Work principle  Mobsafe is a system to check whether an android app is virulence based on some tools in cloud platfarm it will verify the malware.  Mobsafe is an automatize system which can be used to analyze android apps.
  • 7. Infrastructure cloud platform based on CloudStack. Architecture
  • 8. The procedure of Android analysis in Mobsafe.
  • 9.  Mobsafe has a web frontend.  Mobsafe has two backend tools , They are ASEF and SAAF.  ASEF is an automates tool which can be used to analyze Android apps by using ADB and AVD.  SAAF is static analyzer for android apk files.
  • 10. Interest Area As the future perspective some web mining and more advanced data mining techniques will be implemented to get more optimized outputs. I would like to implement Machine learning is a promising technology to identify mobile apps virulence based on data mining.  If I get internship I would like to work on mobile application domain and Testing/