Detecting Malware on the Android Phones Based on Golden Jackal Optimized Support Vector Machine

Authors

  • Rupal Gupta Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Brijraj Singh Solanki Assistant professor, School of Computer Science & System, JAIPUR NAITONAL UNIVERSITY, JAIPUR, India
  • Manish Kumar Assistant Professor, School of Engineering and Computer, Dev Bhoomi Uttarakhand University, Uttarakhand, India,
  • Murugan R. Associate Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India

Keywords:

android mobiles, detecting malware, golden jackal optimized support vector machine (GJOSVM), Android Package files (APK)

Abstract

The Android smartphone's growth may be attributed to the phone's open-source design and high performance. Malware has been created partially because of Android's widespread use. When it comes to smartphones, Android is the most popular OS. That's why there's so much malicious software aimed at this system. Malicious software may be identified as such by analyzing its permission attributes. But this is a complex issue to solve. In this research, we use a golden jackal optimized support vector machine (GJOSVM) to classify software and evaluate whether or not it presents a threat. To achieve this goal, a dataset including 2850 sections of malicious software and 2866 sections of benign software was generated. Each piece of software in the dataset has 112 permission characteristics, and there is also a class feature that indicates whether or not the program is harmful.  Each phase of the training and testing procedures used 10-fold cross-validation. The effectiveness of the models was measured using accuracy, F-1 Score, precision, and recall.

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Published

11.07.2023

How to Cite

Gupta, R. ., Solanki, B. S. ., Kumar, M. ., & R., M. . (2023). Detecting Malware on the Android Phones Based on Golden Jackal Optimized Support Vector Machine . International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 01–07. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3014

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

License

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