K-NEAREST NEIGHBOUR CLASSIFIER USAGE FOR PERMISSION BASED MALWARE DETECTION IN ANDROID

Abstract

ABSTRACT
 Android application platform is making rapid progress in these days. This development has made it the target of malicious application developers. This situation provides a numerical increase in malware apps, diversity in techniques, and rise of damage. Therefore, it is very critical to detect these software and escalation the security of mobile users. Static and dynamic analysis, behaviour scrutiny, machine learning methods are used to ensure security. In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The tests is conducted with dataset includes 492 malware and 697 benign applications. In the proposed algorithm, neighbour number 5 and distance metric is preferred as Minkowski. 80% of dataset randomly selected is reserved for training and 20% for testing. As a result, while 94.1% accuracy is achieved, precision 91.2%, recall 92.7% recall and f1-measure is 92.4%. The high value obtained in f1-measure shows that the proposed model is successful in detecting both malware and benevolent software. The success of using KNN algorithm in classification of malicious apps in the Android has been demonstrated.