Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/16/7538 |
| _version_ | 1827685973089058816 |
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| author | Zakeya Namrud Sègla Kpodjedo Chamseddine Talhi Ahmed Bali Alvine Boaye Belle |
| author_facet | Zakeya Namrud Sègla Kpodjedo Chamseddine Talhi Ahmed Bali Alvine Boaye Belle |
| author_sort | Zakeya Namrud |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T09:01:57Z |
| format | Article |
| id | doaj.art-ff6388118f2c4327bc69fc718cde16e3 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:01:57Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ff6388118f2c4327bc69fc718cde16e32023-11-22T06:43:07ZengMDPI AGApplied Sciences2076-34172021-08-011116753810.3390/app11167538Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities DatasetZakeya Namrud0Sègla Kpodjedo1Chamseddine Talhi2Ahmed Bali3Alvine Boaye Belle4Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaDepartment of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, CanadaDepartment of Electrical Engineering and Computer Science, York University, Toronto, ON M2J 4A6, CanadaAs 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.https://www.mdpi.com/2076-3417/11/16/7538android securitydeep neural networkmachine learningsupport vector machine |
| spellingShingle | Zakeya Namrud Sègla Kpodjedo Chamseddine Talhi Ahmed Bali Alvine Boaye Belle Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset Applied Sciences android security deep neural network machine learning support vector machine |
| title | Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset |
| title_full | Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset |
| title_fullStr | Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset |
| title_full_unstemmed | Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset |
| title_short | Deep Learning Based Android Anomaly Detection Using a Combination of Vulnerabilities Dataset |
| title_sort | deep learning based android anomaly detection using a combination of vulnerabilities dataset |
| topic | android security deep neural network machine learning support vector machine |
| url | https://www.mdpi.com/2076-3417/11/16/7538 |
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