Iterative Classifier Fusion System for the Detection of Android Malware

Abstract

Malicious software (malware) pose serious challenges for security of big data. The number and complexity of malware targeting Android devices have been exponentially increasing with the ever growing popularity of Android devices. To address this problem, multi-classifier fusion systems have long been used to increase the accuracy of malware detection for personal computers. However, previously developed systems are quite large and they cannot be transferred to Android platform. To this end, we propose Iterative Classifier Fusion System (ICFS), which is a system of minimum size, since it applies a smallest possible number of classifiers. The system applies classifiers iteratively in fusion with new iterative feature selection (IFS) procedure. We carry out extensive empirical study to determine the best options to be employed in ICFS and to compare the effectiveness of ICFS with several other traditional classifiers. The experiments show that the best outcomes for Android malware detection have been obtained by the ICFS procedure using LibSVM with polynomial kernel, combined with Multilayer Perceptron and NBtree classifier and applying IFS feature selection based on Wrapper Subset Evaluator with Particle Swarm Optimization.

Existing System

Proposed System

Architecture