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

Namrud, Zakeya, Kpodjedo, Sègla, Talhi, Chamseddine, Bali, Ahmed et 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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Résumé

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.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur Kpodjedo, Sègla Jean-Luc Talhi, Chamseddine
Affiliation:	Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information
Date de dépôt:	17 sept. 2021 20:04
Dernière modification:	16 oct. 2023 18:23
URI:	https://espace2.etsmtl.ca/id/eprint/23139

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