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Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset
| Content Provider | MDPI |
|---|---|
| Author | Namrud, Zakeya Kpodjedo, Sègla Talhi, Chamseddine Bali, Ahmed Belle, Alvine Boaye |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 7538 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11167538 |
| Journal | Applied Sciences |
| Issue Number | 16 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-17 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Information and Library Science Android Security Deep Neural Network Machine Learning Support Vector Machine |
| Content Type | Text |
| Resource Type | Article |
