Mobile Family Detection through Audio Signals Classification

Autor:	Francesco Mercaldo, Fabio Martinelli, Antonella Santone, Giacomo Iadarola, Rosangela Casolare
Rok vydání:	2021
Předmět:	Machine Learning Deep Learning Audio signal Android Computer science Audio Speech recognition Security Classification Malware
Zdroj:	SECRYPT International Conference on Security and Cryptography (SECRYPT 2021), pp. 479–486, online, 02/07/2021 info:cnr-pdr/source/autori:Casolare R.; Iadarola G. (2); Martinelli F. (2); Mercaldo F. (2)(3); Santone A. (3)/congresso_nome:International Conference on Security and Cryptography (SECRYPT 2021)/congresso_luogo:online/congresso_data:02%2F07%2F2021/anno:2021/pagina_da:479/pagina_a:486/intervallo_pagine:479–486
Popis:	Nowadays smartphones, and generically speaking mobile devices, allow users a plethora of tasks in total mobility for instance, from checking the balance on the bank account to distance learning. In this context it is really critical the detection of malicious behaviours, considering the weaknesses of the current antimalware mechanisms. In this paper we propose a method for malicious family detection exploiting audio signal processing: in fact, an application is converted into an audio file and then is processed to generate a feature vector to input several classifiers. We perform a real-world experimental analysis by considering a set of malware targeting the Android platform i.e., 4746 malware belonging to 10 families, showing the effectiveness of the proposed approach for Android malicious family detection.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ab326ed2252accd6ce25d8239ab4aaa https://doi.org/10.5220/0010543504790486 Zobrazit plný text záznamu