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Permission-Based Android Malware Application Detection Using Multi-Layer Perceptron

  • O. S. Jannath NishaEmail author
  • S. Mary Saira Bhanu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

With the increasing number of Android malwares, there arises a need to develop a system that automatically detects malware in Android applications (apps). To discriminate malware applications (malapps) from benign apps, researchers have proposed several detection techniques to detect malicious apps automatically. However, most of these techniques depend on hand-crafted features which are very difficult to analyze. In this paper, the proposed Android Malware Detection approach uses MultiLayer Perceptron (MLP) to discriminate malware apps from benign ones. This approach uses permissions as features based on the static analysis techniques from a disassembled APK file. Apps permission features are automatically learned by the neural network and thereby removing the need for hand-crafted features and are trained to discriminate apps. It is computationally feasible with a large number of dataset samples to perform classification. After the training, the MLP network can be executed, allowing a substantial amount of files to be detected and providing high accurate classification rate. The proposed approach is evaluated using the benchmark dataset. The experimental results show that the proposed approach can achieve high accuracy than that of the existing techniques.

Keywords

Android operating system Benign apps Malapps Permissions Neural networks MLP 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Institute of TechnologyTiruchirappalliIndia

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