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Deep learning based android anomaly detection using a combination of vulnerabilities dataset

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Namrud, Zakeya, Kpodjedo, Sègla, Talhi, Chamseddine, Bali, Ahmed and Belle, Alvine Boaye. 2021. « Deep learning based android anomaly detection using a combination of vulnerabilities dataset ». Applied Sciences, vol. 11, nº 16.
Compte des citations dans Scopus : 2.

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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.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Kpodjedo, Sègla Jean-Luc
Talhi, Chamseddine
Affiliation: Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information
Date Deposited: 17 Sep 2021 20:04
Last Modified: 16 Oct 2023 18:23
URI: https://espace2.etsmtl.ca/id/eprint/23139

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