Analysis of Data Mining Tools for Android Malware Detection

  • Robiah Yusof
  • Nurul Syahirrah Adnan
  • Nurlaily Abd. Jalil
  • Raihana Syahirah Abdullah

Abstract

There are various data mining tools available to analyze data related android malware detection. However, the problem arises in deciding the most appropriate machine learning techniques or algorithm on particular tools to be implemented on particular data. This research is focusing only on classification techniques. Hence, the objective of this research is to identify the best machine learning technique or algorithm on selected tool for android malware detection. Five techniques: Random Forest, Naive Bayes, Support Vector Machine, Forest, K-Nearest Neighbour and Adaboost are selected and applied in selected tools namely Weka and Orange. The result shows that Adaboost technique in Weka tool and Random Forest technique in Orange tool has obtained accuracy above 80% compare to other techniques. This result provides an option for the researcher on applying technique or algorithm on selected tool when analyzing android malware data.

Author Biographies

Robiah Yusof

Centre for Advanced Computing Technology, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka

Nurul Syahirrah Adnan

Centre for Advanced Computing Technology, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka

Nurlaily Abd. Jalil

Centre for Advanced Computing Technology, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka

Raihana Syahirah Abdullah

Centre for Advanced Computing Technology, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka

Published
2019-11-30
How to Cite
Yusof, R., Adnan, N. S., Abd. Jalil, N., & Abdullah, R. S. (2019). Analysis of Data Mining Tools for Android Malware Detection. Journal of Advanced Computing Technology and Application (JACTA), 1(2), 21-24. Retrieved from https://jacta.utem.edu.my/jacta/article/view/5196
Section
Articles