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Android Rogue Application Detection Using Image Resemblance and Reduced LDA

  • Saket AcharyaEmail author
  • Umashankar Rawat
  • Roheet Bhatnagar
Conference paper
  • 60 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录nowadays, the expanding diffusion of android phones along with the substantial usage of mobile applications is increasing the malware production. among various malware threats, the rogue applications have expanded their growth in the field of smartphones, especially android phones. this paper presents an optimal methodology to detect and classify rogue applications using image resemblance and opcode sequence reduction. first, the opcode sequences are extracted, and then, they are converted into gray images. after this, linear discriminant analysis (lda) is applied in two stages. lda is a supervised probabilistic method that is used for class separation and size reduction. in the first stage, the image sizes are reduced by selecting only the optimal features using lda. the main objective of this stage is to increase the accuracy rate by reducing the size of opcode sequences. in the next stage, lda is applied to test and train the dataset samples for separating rogue and benign apps. the experimental results on the rogue application families and unknown rogue apps show that the proposed methodology is efficiently able to identify rogue apps with an accuracy rate of 96.5%.

Keywords

Machine learning Image similarity Classification Android malware Rogue application Android security Linear discriminant analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Saket Acharya
    • 1
    Email author
  • Umashankar Rawat
    • 1
  • Roheet Bhatnagar
    • 1
  1. 1.Manipal University JaipurJaipurIndia

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