Main Reference PaperIterative Classifier Fusion System for the Detection of Android Malware, IEEE Transactions on Big Data, March 2017 [Java].
  • An effective android compatible classification system with minimum number of classifiers is proposed to detect android malware accurately that operates iteratively in fusion with new iterative feature selection (IFS) procedure and work in better manner against existing classifiers.

+ Description
  • An effective android compatible classification system with minimum number of classifiers is proposed to detect android malware accurately that operates iteratively in fusion with new iterative feature selection (IFS) procedure and work in better manner against existing classifiers.

  • To increase the accuracy of malware detection for personal computers.

  • To design effective android compatible classification system with the optimal number of classifiers.

+ Aim & Objectives
  • To increase the accuracy of malware detection for personal computers.

  • To design effective android compatible classification system with the optimal number of classifiers.

  • Accuracy of the system is improved further through effective and light weight data mining technique.

+ Contribution
  • Accuracy of the system is improved further through effective and light weight data mining technique.

  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1.

  • Netbeans 8.0.1 & J2EE.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1.

  • Netbeans 8.0.1 & J2EE.

  • 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-Depending on the complexity of the project and requirements.

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

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