Classification and security assessment of android apps

Abstract Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Eralda Caushaj [verfasserIn] Vijayan Sugumaran [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Authorization Application permission Security Privacy Android platform Human factors

Übergeordnetes Werk:	In: Discover Internet of Things - Springer, 2021, 3(2023), 1, Seite 17
Übergeordnetes Werk:	volume:3 ; year:2023 ; number:1 ; pages:17

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1007/s43926-023-00047-0

Katalog-ID:	DOAJ092876943

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520			\|a Abstract Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware all require access to mobile resources and data to function properly. Therefore, it is difficult for the user to make informed decisions that effectively balance their privacy and app functionality. This study analyzes the Android application permissions, ad networks and the impact on end-user’s privacy. Dangerous combinations of app permissions, and ad networks are used as features in our prediction models to understand the behavior of apps. Our models have a high classification accuracy of 95.9% considering the imbalance in real life between benign and malicious apps. Our assumption that certain app permissions can be a potential threat to the privacy of end users is confirmed to be one of the most impactful features of our prediction models. Since our study considers the impact of ad networks and malware permissions, it will help end-users make more informed decision about the app permissions they grant and understand that the app permissions open doors to more vulnerabilities, and at some point, benign apps can behave maliciously.
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allfieldsSound	10.1007/s43926-023-00047-0 doi (DE-627)DOAJ092876943 (DE-599)DOAJ6fa77a5b038c4c9780377a8259bdb5a3 DE-627 ger DE-627 rakwb eng TK7885-7895 QA76.75-76.765 Eralda Caushaj verfasserin aut Classification and security assessment of android apps 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware all require access to mobile resources and data to function properly. Therefore, it is difficult for the user to make informed decisions that effectively balance their privacy and app functionality. This study analyzes the Android application permissions, ad networks and the impact on end-user’s privacy. Dangerous combinations of app permissions, and ad networks are used as features in our prediction models to understand the behavior of apps. Our models have a high classification accuracy of 95.9% considering the imbalance in real life between benign and malicious apps. Our assumption that certain app permissions can be a potential threat to the privacy of end users is confirmed to be one of the most impactful features of our prediction models. Since our study considers the impact of ad networks and malware permissions, it will help end-users make more informed decision about the app permissions they grant and understand that the app permissions open doors to more vulnerabilities, and at some point, benign apps can behave maliciously. Authorization Application permission Security Privacy Android platform Human factors Computer engineering. Computer hardware Computer software Vijayan Sugumaran verfasserin aut In Discover Internet of Things Springer, 2021 3(2023), 1, Seite 17 (DE-627)1750132583 27307239 nnns volume:3 year:2023 number:1 pages:17 https://doi.org/10.1007/s43926-023-00047-0 kostenfrei https://doaj.org/article/6fa77a5b038c4c9780377a8259bdb5a3 kostenfrei https://doi.org/10.1007/s43926-023-00047-0 kostenfrei https://doaj.org/toc/2730-7239 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2023 1 17
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callnumber-first	T - Technology
author	Eralda Caushaj
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title	Classification and security assessment of android apps
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abstract	Abstract Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware all require access to mobile resources and data to function properly. Therefore, it is difficult for the user to make informed decisions that effectively balance their privacy and app functionality. This study analyzes the Android application permissions, ad networks and the impact on end-user’s privacy. Dangerous combinations of app permissions, and ad networks are used as features in our prediction models to understand the behavior of apps. Our models have a high classification accuracy of 95.9% considering the imbalance in real life between benign and malicious apps. Our assumption that certain app permissions can be a potential threat to the privacy of end users is confirmed to be one of the most impactful features of our prediction models. Since our study considers the impact of ad networks and malware permissions, it will help end-users make more informed decision about the app permissions they grant and understand that the app permissions open doors to more vulnerabilities, and at some point, benign apps can behave maliciously.
abstractGer	Abstract Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware all require access to mobile resources and data to function properly. Therefore, it is difficult for the user to make informed decisions that effectively balance their privacy and app functionality. This study analyzes the Android application permissions, ad networks and the impact on end-user’s privacy. Dangerous combinations of app permissions, and ad networks are used as features in our prediction models to understand the behavior of apps. Our models have a high classification accuracy of 95.9% considering the imbalance in real life between benign and malicious apps. Our assumption that certain app permissions can be a potential threat to the privacy of end users is confirmed to be one of the most impactful features of our prediction models. Since our study considers the impact of ad networks and malware permissions, it will help end-users make more informed decision about the app permissions they grant and understand that the app permissions open doors to more vulnerabilities, and at some point, benign apps can behave maliciously.
abstract_unstemmed	Abstract Current mobile platforms pose many privacy risks for the users. Android applications (apps) request access to device resources and data, such as storage, GPS location, camera, microphone, SMS, phone identity, and network information. Legitimate mobile apps, advertisements (ads), and malware all require access to mobile resources and data to function properly. Therefore, it is difficult for the user to make informed decisions that effectively balance their privacy and app functionality. This study analyzes the Android application permissions, ad networks and the impact on end-user’s privacy. Dangerous combinations of app permissions, and ad networks are used as features in our prediction models to understand the behavior of apps. Our models have a high classification accuracy of 95.9% considering the imbalance in real life between benign and malicious apps. Our assumption that certain app permissions can be a potential threat to the privacy of end users is confirmed to be one of the most impactful features of our prediction models. Since our study considers the impact of ad networks and malware permissions, it will help end-users make more informed decision about the app permissions they grant and understand that the app permissions open doors to more vulnerabilities, and at some point, benign apps can behave maliciously.
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