Publication: A new mobile botnet classification based on permission and API calls
No Thumbnail Available
Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Currently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters. � 2017 IEEE.
Description
Keywords
android botnet, machine learning, mobile botnet classification, random forest algorithm, static analysis, Android (operating system), Application programming interfaces (API), Artificial intelligence, Botnet, Classification (of information), Decision trees, Learning systems, Smartphones, Static analysis, Botnet detections, Detection accuracy, End users, False positive rates, Google plays, Mobile applications, Random forest algorithm, Training dataset, Learning algorithms