Malware Detection in Android Operating System using Convolutional Neural Network and Long Short-Term Memory Network

Document Type : Computer Article

Authors

1 Department of Computer Sciences, Faculty of Sciences, Golestan University, Gorgan, Iran

2 Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

Abstract

The use of mobile phones with Android operating system is expanding day by day. Android itself does not have a powerful malware detection tool. Therefore, attackers easily enter people's privacy through their mobile phones and put them at serious risk. So far, a lot of research has been done on malware detection. One of the main problems of these solutions is the low accuracy in multi-class detection on the dataset or the failure to achieve the desired result in both types of binary and multi-class detection. In this paper, by using Convolutional Neural Network (CNN) and changing the number of different layers, we have tried to extract the maximum number of important features from the dataset. In the data classification phase, we use the Deep Learning-based algorithm named Long Short-Term Memory (LSTM) to classify the data with the maximum possible accuracy by testing it on the selected features. The test results on the new MalMemAnalysis-2022 dataset show that the use of these two algorithms and the change in the number of layers can lead to 99.99% and 99.71% accuracies in binary and multi-class classification in malware detection, respectively, which is superior to existing methods.

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References

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Journal of Modeling in Engineering
Volume 23, Issue 80
In Progress
March 2025
Pages 47-57

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History

  • Receive Date: 29 September 2023
  • Revise Date: 08 June 2024
  • Accept Date: 11 June 2024

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