In today’s busy world, usage of mobile applications is increasing in all aspects of life including banking and finance. Taking this as an opportunity, cyber crimes are taking place in the form of hacking and malware. While installing the mobile apps, everyone may not be aware of which permissions to accept and which one to deny. If the user start accepting all permissions, malware or malicious apk files may enter the mobile through some of them. Many machine learning techniques have been introduced to resolve this problem but failed to get considerable accuracy in real-time applications. The present research concentrates on detecting the malware which can enter through permissions in android using deep neural network model. The proposed approach detects the permission driven malware in real time android apk files with more than 85% accuracy.


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    Title :

    Detection of Permission Driven Malware in Android Using Deep Learning Techniques


    Contributors:


    Publication date :

    2019-06-01


    Size :

    1285455 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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