Pregled bibliografske jedinice broj: 816712
Evaluation of Android Malware Detection Based on System Calls
Evaluation of Android Malware Detection Based on System Calls // Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics
New York (NY): The Association for Computing Machinery (ACM), 2016. str. 1-8 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Evaluation of Android Malware Detection Based on System Calls
Autori
Dimjašević, Marko ; Atzeni, Simone ; Ugrina, Ivo ; Rakamarić, Zvonimir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics
/ - New York (NY) : The Association for Computing Machinery (ACM), 2016, 1-8
ISBN
978-1-4503-4077-9
Skup
IWSPA ’16 (2016 ACM on International Workshop on Security And Privacy Analytics)
Mjesto i datum
New Orleans (LA), Sjedinjene Američke Države, 09.04.2016. - 11.04.2016
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Android; Malware; System Call
Sažetak
With Android being the most widespread mobile platform, protecting it against malicious applications is essential. Android users typically install applications from large remote repositories, which provides ample opportunities for malicious newcomers. In this paper, we evaluate a few techniques for detecting malicious Android applications on a repository level. The techniques perform automatic classification based on tracking system calls while applications are executed in a sandbox environment. We implemented the techniques in the MALINE tool, and performed extensive empirical evaluation on a suite of around 12, 000 applications. The evaluation considers the size and type of inputs used in analyses. We show that simple and relatively small inputs result in an overall detection accuracy of 93% with a 5% benign application classification error, while results are improved to a 96% detection accuracy with upsampling. Finally, we show that even simplistic feature choices are effective, suggesting that more heavyweight approaches should be thoroughly (re)evaluated.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo