Visible to the public On the Effectiveness of Application Permissions for Android Ransomware Detection

TitleOn the Effectiveness of Application Permissions for Android Ransomware Detection
Publication TypeConference Paper
Year of Publication2020
AuthorsAlsoghyer, Samah, Almomani, Iman
Conference Name2020 6th Conference on Data Science and Machine Learning Applications (CDMA)
Date Publishedmar
KeywordsAccuracy, Analytical models, android, applications, Computer science, data mining, dataset, detection, feature extraction, Human Behavior, Internet, Malware, pattern locks, permissions, Predictive models, pubcrawl, ransomware, Resiliency, Scalability, security
AbstractRansomware attack is posting a serious threat against Android devices and stored data that could be locked or/and encrypted by such attack. Existing solutions attempt to detect and prevent such attack by studying different features and applying various analysis mechanisms including static, dynamic or both. In this paper, recent ransomware detection solutions were investigated and compared. Moreover, a deep analysis of android permissions was conducted to identify significant android permissions that can discriminate ransomware with high accuracy before harming users' devices. Consequently, based on the outcome of this analysis, a permissions-based ransomware detection system is proposed. Different classifiers were tested to build the prediction model of this detection system. After the evaluation of the ransomware detection service, the results revealed high detection rate that reached 96.9%. Additionally, the newly permission-based android dataset constructed in this research will be made available to researchers and developers for future work.
Citation Keyalsoghyer_effectiveness_2020