Malware Analysis on Android Apps: A Permission-based Approach
Abstract
The use of Android devices nowadays is almost inevitable. Having been able to get a big slice of the mobile operating systems, Android has become a wide target for malware attacks. Malware detection analysis in this study is done to contribute to the many various ways in doing the malware analysis using classification algorithm using Random Forest and Naive Bayesian. This study used a static method of analyzing and detecting malware applications through the permission requests made by each Android applications as analyzed by VirusTotal website. This study utilized fifty actual Android samples down- loaded from the Internet in which the samples were composed of twenty-five benign apps and twenty-five malware applications.
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Yamauchi, T. (2013). DroidTrack: Tracking
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Analysis for Android Mal- ware Detection.
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Android Malware Detection Using Parallel Machine
Learning Classifiers, (Ngmast), 1014.
[26] Braehler S (2010). Analysis of Android
Architecture.
[27] Frank, M., Dong, B., Felt, A. P., Song, D. (2012).
Mining Permission Request Patterns from Android
and Facebook Applications.
[28] Online Malware/Spareware Scanning
https://www.virustotal.com. Re- trieved: Sept 1,
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Android Malware by Analyzing Manifest Files.
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[30] https://www.educba.com/text-mining/. Date
Retrieved: August 25, 2016.
Ariel O. Gamao is a DIT student of Technological
Institute of the Philippines, Quezon City. The author
is currently enrolled of a DIT Program through an offsite
campus hosted by the University of Mindanao,
Matina, Davao City. Mr. Gamao is also a faculty of
the Davao del Norte State College, Panabo City who
at the same time a Ph.D in Educational Graduate.
R. (2014). Malware Classification using Nave Bayes
Classifier for Android OS, 5963.
[2] Wei X, Gomez L, Neamtiu I. and Faloutsos M.,
”Permission evolution in the Android ecosystem,” in
Proc. of Computer Security Applications Conference,
31-40, 2012. Article(CrossRefLink).
[3] Ye, Y., Wu, L., Hong, Z., Huang, K. (2017). A
risk classification based approach for Android
malware detection. KSII Transactions on Internet and
Information Systems.
[4] IDC: Smartphone OS Market share
http://www.idc.com/promo/smartphone-marketshare/
os. July 29, 2017.
2017 Retrieved:
[5] Rashidi, B., Fung, C. (2016). XDroid: An
Android Permission Control Using Hidden Markov
Chain and Online Learning.
[6]. Verma, S., Muttoo, S. K. (2016). An Android
Malware Detection Framework-based on
Permissions and Intents, 66(6), 618623.
[7]. Zhao, M., Zhang, T., Wang, J., Yuan, Z. (2013).
A smartphone malware detection framework based
on artificial immunology. Journal of Networks.
[8]. Au K W Y, Zhou Y F, Huang Z, Lie D., ”PScout:
analyzing the Android permission specification,” in
Proc. of the 2012 ACM conference on Computer and
communications security. ACM, 217-228, 2012.
Arti- cle(CrossRefLink).
[9]. Johnson R, Wang Z, Gagnon C and Stavrou, ”A.
Analysis of Android Applications’ Permissions,” in
Proc. of IEEE Sixth International Con- ference on
Software Security and Reliability Companion. 45-46,
2012. Article(CrossRefLink).
[10]. Barrera D, Kayacik, H. G, Van Oorschot P C
and Somayaji A., ”A methodology for empirical
analysis of permission-based security models and its
application to android,” in Proc. of ACM Conference
on Computer and Communications Security, CCS
2010, Chicago, Illinois, USA, October. 73-84, 2010.
Article(CrossRefLink).
[11]. Felt A P, Chin E, Hanna S, Song D and Wagner
D., ”Android permissions demystified,” in Proc. of
ACM Conference on Computer and Communications
Security, CCS 2011, Chicago, Illinois, USA,
October. 627-638, 2011. Article(CrossRefLink).
[12]. Nauman M, Khan S, Zhang X., ”Apex:
Extending Android Permission Model and
Enforcement with User-defined Runtime
Constraints,” in Proc. of ACM Symposium on
Information, Computer and Communi- cations
Security, ASIACCS 2010, Beijing, China, April.
328-332, 2010. Article(CrossRefLink).
[13]. Felt A P, Wang H J, Moshchuk A, Hanna S and
Chin E., ”Permission re-delegation: attacks and
defenses,” Usenix Conference on Security. USENIX
Association, 22-22, 2011. Article(CrossRefLink).
[14] Dietz M, Shekhar S, Pisetsky Y, Shu A and
Wallach DS., ”Quire: lightweight provenance for
smart phone operating systems,” Dissertations
Theses, 23-23, 2011. Article(CrossRefLink)
[15]. Bugiel S, Davi L, Dmitrienko A, Fischer T and
Sadeghi AR., ”XManAndroid: A new Android
evolution to mitigate privilege escalation attacks,”
Technical Report, Technische Universitat Darmstadt,
TR-2011- 04, 2011. Article(CrossRefLink).
[16]. Sakamoto S, Okuda K, Nakatsuka R and
Yamauchi T., ”DroidTrack: tracking and visualizing
information diffusion for preventing information
leakage on Android,” Journal of Internet.
[17]. Tchakounte, F. (2014). Permission-based
malware detection mecha- nisms on Android:
analysis and perspectives. Journal of Computer
Science and Software Application, 1(2), 6377.
[18] Seth, R., Kaushal, R. (2015). Permission based
Malware Analysis Detection in Android.
[19]. Sakamoto, S., Okuda, K., Nakatsuka, R.,
Yamauchi, T. (2013). DroidTrack: Tracking
information diffusion and preventing information
leakage on android.
[20]. A. Apvrille and T. Strazzere, Reducing the
window of opportunity for Android malware Gotta
catch em all, Journal in Computer Virology vol. 8,
No. 1-2, pp. 61-71, 2012.
[21]. Ye, Y., Wu, L., Hong, Z., Huang, K. (2017). A
risk classification based approach for Android
malware detection. KSII Transactions on Internet and
Information Systems.
[22]. Kapratwar, A. (2016). Static and Dynamic
Analysis for Android Mal- ware Detection.
[23] Uur, P. (2014). Permission-Based Malware
Detection Analysis in An- droid Applications.
[24] Sanz, B., Santos, I., Laorden, C., Ugarte-
Pedrero, X., Nieves, J., Bringas, P. G., lvarez Maran,
G. (2013). Mama: Manifest Analysis for Malware
Detection in Android. Cybernetics and Systems,
44(67), 469488.
https://doi.org/10.1080/01969722.2013.803889.
[25] Yerima, S. Y., Sezer, S., Muttik, I. (2014).
Android Malware Detection Using Parallel Machine
Learning Classifiers, (Ngmast), 1014.
[26] Braehler S (2010). Analysis of Android
Architecture.
[27] Frank, M., Dong, B., Felt, A. P., Song, D. (2012).
Mining Permission Request Patterns from Android
and Facebook Applications.
[28] Online Malware/Spareware Scanning
https://www.virustotal.com. Re- trieved: Sept 1,
2017.
[29] Sato, R., Chiba, D., Goto, S. (2013). Detecting
Android Malware by Analyzing Manifest Files.
Proceedings of the Asia-Pacific Advanced Network,
36, 23. https://doi.org/10.7125/APAN.36.4.
[30] https://www.educba.com/text-mining/. Date
Retrieved: August 25, 2016.
Ariel O. Gamao is a DIT student of Technological
Institute of the Philippines, Quezon City. The author
is currently enrolled of a DIT Program through an offsite
campus hosted by the University of Mindanao,
Matina, Davao City. Mr. Gamao is also a faculty of
the Davao del Norte State College, Panabo City who
at the same time a Ph.D in Educational Graduate.
Authors
[1]
“Malware Analysis on Android Apps: A Permission-based Approach”, Soc. sci. humanities j., vol. 2, no. 10, pp. 624–633, Oct. 2018, Accessed: Apr. 20, 2024. [Online]. Available: https://sshjournal.com/index.php/sshj/article/view/240
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