ANDROID MALWARE DETECTION USING ENSEMBLE LEARNING APPROACH
Keywords:
Cybersecurity, malware detection, ensemble learning, hunter prey optimization, machine learning.Abstract
Android platform due to open-source characteristics and Google backing has the largest global
market share. Being the world’s most popular operating system, it has drawn the attention of cyber criminals
operating particularly through the wide distribution of malicious applications. This paper proposes an effectual
machine-learning-based approach for Android Malware Detection making use of an evolutionary chi-square
algorithm for discriminatory feature selection. Selected features from the chi-square algorithm are used to train
machine learning classifiers and their capability in identification of Malware before and after feature selection is
compared. The experimentation results validate that the chi-square algorithm gives the most optimized feature
subset helping in the reduction of feature dimension to less than half of the original feature set. Classification
accuracy of more than the previous percentage is maintained post-feature selection for the machine learningbased
classifiers, while working on much reduced feature dimension, thereby, having a positive impact on the
computational complexity of learning classifiers
