Main Reference PaperMalDozer: Automatic framework for android malware detection using deep learning, Digital Investigation, 2018 [Python]
  • The MalDozer framework identifies the malware detection in Android on the basis of sequence mining by employing the neural networks. It receives the raw API method calls sequence as input to ensure the family attribution and malware detection.

Description
  • The MalDozer framework identifies the malware detection in Android on the basis of sequence mining by employing the neural networks. It receives the raw API method calls sequence as input to ensure the family attribution and malware detection.

  • To discover the Android malware

  • To obtain great detection accuracy

Aim & Objectives
  • To discover the Android malware

  • To obtain great detection accuracy

  • To contribute the technique to improve the effectiveness in family attribution

Contribution
  • To contribute the technique to improve the effectiveness in family attribution

  • 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.

Professional Ethics: We S-Logix would appreciate the students those who willingly contribute with atleast a line of thinking of their own while preparing the project with us. It is advised that the project given by us be considered only as a model project and be applied with confidence to contribute your own ideas through our expert guidance and enrich your knowledge.

Leave Comment

Your email address will not be published. Required fields are marked *

clear formSubmit