CyCon 2019 in Tallinn

28th - 31st May 2019, Tallinn

Neural Network-Based Technique for Android Smartphone Applications Classification

At the International Conference on Cyber Conflict (CyCon) Roman Graf, Ross King and Aaron Kaplan offer effective identifying and defeating methodologies for malware applications in Android smartphones.

Abstract: With the booming development of smartphone capabilities, these devices are increasingly frequent victims of targeted attacks in the ‘silent battle’ of cyberspace. Protecting Android smartphones against the increasing number of malware applications has become as crucial as it is complex. To be effective in identifying and defeating malware applications, cyber analysts require novel distributed detection and reaction methodologies  based  on  information  security  techniques  that  can  automatically analyse  new  applications  and  share  analysis  results  between  smartphone  users.  Our goal is to provide a real-time solution that can extract application features and find related correlations within an aggregated knowledge base in a fast and scalable way, and to automate the classification of Android smartphone applications. Our effective and fast application analysis method is based on artificial intelligence and can support smartphone  users  in  malware  detection  and  allow  them  to  quickly  adopt  suitable countermeasures  following  malware  detection.  In  this  paper,  we  evaluate  a  deep neural  network  supported  by  word-embedding  technology  as  a  system  for  malware application  classification  and  assess  its  accuracy  and  performance. This  approach should reduce the number of infected smartphones and increase smartphone security. We demonstrate how the presented techniques can be applied to support smartphone application classification tasks performed by smartphone users.