Cargando…
Automated Android Malware Detection Using User Feedback
The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hos...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460230/ https://www.ncbi.nlm.nih.gov/pubmed/36081021 http://dx.doi.org/10.3390/s22176561 |
_version_ | 1784786696257142784 |
---|---|
author | Duque, João Mendes, Goncalo Nunes, Luís de Almeida, Ana Serrão, Carlos |
author_facet | Duque, João Mendes, Goncalo Nunes, Luís de Almeida, Ana Serrão, Carlos |
author_sort | Duque, João |
collection | PubMed |
description | The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed in marketplaces that host millions and therefore have their forms of automated malware detection in place to deter malware developers and keep their app store (and reputation) trustworthy. Nevertheless, a non-negligible number of apps can bypass these detectors and remain available in the marketplace for any user to download and install on their device. Current malware detection strategies rely on using static or dynamic app extracted features (or a combination of both) to scale the detection and cover the growing number of apps submitted to the marketplace. In this paper, the main focus is on the apps that bypass the malware detectors and stay in the marketplace long enough to receive user feedback. This paper uses real-world data provided by an app store. The quantitative ratings and potential alert flags assigned to the apps by the users were used as features to train machine learning classifiers that successfully classify malware that evaded previous detection attempts. These results present reasonable accuracy and thus work to help to maintain a user-safe environment. |
format | Online Article Text |
id | pubmed-9460230 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94602302022-09-10 Automated Android Malware Detection Using User Feedback Duque, João Mendes, Goncalo Nunes, Luís de Almeida, Ana Serrão, Carlos Sensors (Basel) Article The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed in marketplaces that host millions and therefore have their forms of automated malware detection in place to deter malware developers and keep their app store (and reputation) trustworthy. Nevertheless, a non-negligible number of apps can bypass these detectors and remain available in the marketplace for any user to download and install on their device. Current malware detection strategies rely on using static or dynamic app extracted features (or a combination of both) to scale the detection and cover the growing number of apps submitted to the marketplace. In this paper, the main focus is on the apps that bypass the malware detectors and stay in the marketplace long enough to receive user feedback. This paper uses real-world data provided by an app store. The quantitative ratings and potential alert flags assigned to the apps by the users were used as features to train machine learning classifiers that successfully classify malware that evaded previous detection attempts. These results present reasonable accuracy and thus work to help to maintain a user-safe environment. MDPI 2022-08-31 /pmc/articles/PMC9460230/ /pubmed/36081021 http://dx.doi.org/10.3390/s22176561 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Duque, João Mendes, Goncalo Nunes, Luís de Almeida, Ana Serrão, Carlos Automated Android Malware Detection Using User Feedback |
title | Automated Android Malware Detection Using User Feedback |
title_full | Automated Android Malware Detection Using User Feedback |
title_fullStr | Automated Android Malware Detection Using User Feedback |
title_full_unstemmed | Automated Android Malware Detection Using User Feedback |
title_short | Automated Android Malware Detection Using User Feedback |
title_sort | automated android malware detection using user feedback |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460230/ https://www.ncbi.nlm.nih.gov/pubmed/36081021 http://dx.doi.org/10.3390/s22176561 |
work_keys_str_mv | AT duquejoao automatedandroidmalwaredetectionusinguserfeedback AT mendesgoncalo automatedandroidmalwaredetectionusinguserfeedback AT nunesluis automatedandroidmalwaredetectionusinguserfeedback AT dealmeidaana automatedandroidmalwaredetectionusinguserfeedback AT serraocarlos automatedandroidmalwaredetectionusinguserfeedback |