A machine learning approach to anomaly-based detection on Android platforms
This addresses the problem of inefficient signature-based detection for new and unknown malware on mobile devices, which is critical for users of sensitive applications like online banking, though it appears incremental as it applies an existing classifier to this domain.
The paper tackles malware detection on Android platforms by presenting a machine learning approach that monitors and extracts features from applications during execution, achieving an accuracy of 93.75% and a low error rate of 6.25%.
The emergence of mobile platforms with increased storage and computing capabilities and the pervasive use of these platforms for sensitive applications such as online banking, e-commerce and the storage of sensitive information on these mobile devices have led to increasing danger associated with malware targeted at these devices. Detecting such malware presents inimitable challenges as signature-based detection techniques available today are becoming inefficient in detecting new and unknown malware. In this research, a machine learning approach for the detection of malware on Android platforms is presented. The detection system monitors and extracts features from the applications while in execution and uses them to perform in-device detection using a trained K-Nearest Neighbour classifier. Results shows high performance in the detection rate of the classifier with accuracy of 93.75%, low error rate of 6.25% and low false positive rate with ability of detecting real Android malware.