| Title | On the Effectiveness of Application Permissions for Android Ransomware Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Alsoghyer, Samah, Almomani, Iman |
| Conference Name | 2020 6th Conference on Data Science and Machine Learning Applications (CDMA) |
| Date Published | mar |
| Keywords | Accuracy, 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 |
| Abstract | Ransomware 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. |
| DOI | 10.1109/CDMA47397.2020.00022 |
| Citation Key | alsoghyer_effectiveness_2020 |
On the Effectiveness of Application Permissions for Android Ransomware Detection