Malware Detection and Classification Using Latent Semantic Indexing - Parajuli, Srijana; Shakya, Subarna (Journal of Advanced College of Engineering and Management, 2018)
The increasing popularity of smart phones has led to the dramatic growth in mobile malware especially in Android platform. Many aspects of android permission has been studied for malware detection but sufficient attention has not been given to intent. This research work proposes using Latent Semantic Indexing for malware detection and classification with permissions and intents based features. This method analyses the Manifest file of an android application by understanding the risk level of permission and intents and assigning weight score based on their sensitivity. In an experiment conducted using a dataset containing 400 malware samples and 400 normal/benign samples, the results show accuracy of 83.5% using Android Intent against 79.1 % using Android permission. Additionally, experiment on combination of both features results in accuracy of 89.7%. It can be concluded from this research work that dataset with intent based features is able to detect malwares more when compared to permissions based features.