Mobile botnets detection using deep learning techniques

Document Type : Original Article

Authors

1 Master's student, Shahid Bahonar University of Kerman, Kerman, Iran

2 Associate Professor, Shahid Bahonar University of Kerman, Kerman, Iran

3 Assistant Professor, Shahid Bahonar University of Kerman, Kerman, Iran

Abstract

Smartphones are now well integrated with advanced capabilities and technologies such as the Internet. Today, due to the facilities and capabilities and the widespread use of smart mobile devices, mobile security has become a vital issue worldwide. Smartphones are not properly protected compared to computers and computer networks, and users do not consider security updates. Recently, mobile devices and networks have been targeted by one of the most dangerous cyber threats known as botnets. Mobile Bantent An enhanced example of Boutons has the ability to perform malicious operations such as denial of service attacks, data theft, eavesdropping, and more. Bunters use three communication protocols: HTTP, SMS and Bluetooth to communicate with each other; So when users are not connected to the Internet, botnets are able to communicate with each other. In this study, to identify mobile batonet from 14 Android baton families, including 1932 samples of Android mobile devices applications and 4304 samples of safe and secure Android mobile devices applications have been used. Application permissions were extracted for reverse engineering to automatically classify and detect types of botnets, then based on these permissions, each application was converted to an equivalent image using the proposed method. Labeled images were then used to train convolutional neural networks. The results of evaluation and comparison of this method with classical methods including backup vector machine and decision tree showed that the proposed method is able to achieve higher efficiency in detecting different types of botnets and separating it from healthy programs

Keywords

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References

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Electronic and Cyber Defense
Volume 11, Issue 2 - Serial Number 42
No. 42, Summer
July 2023
Pages 31-43

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History

  • Receive Date: 12 June 2022
  • Revise Date: 20 January 2023
  • Accept Date: 17 May 2023
  • Publish Date: 22 June 2023

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