Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset

Abstract

As the leading mobile phone operating system, Android is an attractive target for malicious applications trying to exploit the system’s security vulnerabilities. Although several approaches have been proposed in the research literature for the detection of Android malwares, many of them suffer from issues such as small training datasets, there are few features (most studies are limited to permissions) that ultimately affect their performance. In order to address these issues, we propose an approach combining advanced machine learning techniques and Android vulnerabilities taken from the AndroVul dataset, which contains a novel combination of features for three different vulnerability levels, including dangerous permissions, code smells, and AndroBugs vulnerabilities. Our approach relies on that dataset to train Deep Learning (DL) and Support Vector Machine (SVM) models for the detection of Android malware. Our results show that both models are capable of detecting malware encoded in Android APK files with about 99% accuracy, which is better than the current state-of-the-art approaches.

Green Requirement Engineering: Towards Sustainable Mobile Application Development and Internet of Things

“…When such a situation occurs, Android application developers frequently employ the short messaging service to provide additional One-Time Password (OTP) authentication to strengthen the Password Authentication Protocol (PAP) (SMS). However, SMS is not designed to be a secure service [101,102].…”