Main Reference PaperA machine learning based approach to detect malicious android apps using discriminant system calls, Future Generation Computer Systems, 2019 [Java/Python/R]
  • The proposed method introduces the Absolute Difference of Weighted System Calls (ADWSC) and Ranked System Calls using Large Population Test (RSLPT) feature selection technique for pruning large volume system call set that yields relevant feature for next step classification to identify the malware samples.

+ Description
  • The proposed method introduces the Absolute Difference of Weighted System Calls (ADWSC) and Ranked System Calls using Large Population Test (RSLPT) feature selection technique for pruning large volume system call set that yields relevant feature for next step classification to identify the malware samples.

  • To identify the malicious application in the android

  • To minimize the computational complexity

+ Aim & Objectives
  • To identify the malicious application in the android

  • To minimize the computational complexity

  • The feature selection based on the correlation between the feature will improve the system accuracy.

+ Contribution
  • The feature selection based on the correlation between the feature will improve the system accuracy.

  • Operating system: Ubuntu / Windows

  • Java/Python/R

+ Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Java/Python/R

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

+ Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

+ Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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