Analysis of Data Mining Tools for Android Malware Detection

• Main Authors: Yusof, Robiah, Abdullah, Raihana Syahirah, Adnan, Nurul Syahirrah, Abd. Jalil, Nurlaily
• 格式: Article
• 語言: English
• 出版: Faculty Of Information And Communication Technology, UTeM 2019
• 在線閱讀: http://eprints.utem.edu.my/id/eprint/24018/1/http%3A//portal.utem.edu.my/iURIS/uploadfile/Journal/00763/Analysis%20of%20Data%20mining%20tools%20for%20Android%20Malware%20Detections.pdf; http://eprints.utem.edu.my/id/eprint/24018/; https://jacta.utem.edu.my/jacta/article/view/5196

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.