CFP last date
20 June 2024
Reseach Article

Malware Detection Techniques in Android

by Pallavi Kaushik, Amit Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 17
Year of Publication: 2015
Authors: Pallavi Kaushik, Amit Jain

Pallavi Kaushik, Amit Jain . Malware Detection Techniques in Android. International Journal of Computer Applications. 122, 17 ( July 2015), 22-26. DOI=10.5120/21794-5166

@article{ 10.5120/21794-5166,
author = { Pallavi Kaushik, Amit Jain },
title = { Malware Detection Techniques in Android },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 17 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-26 },
numpages = {9},
url = { },
doi = { 10.5120/21794-5166 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:11:27.157477+05:30
%A Pallavi Kaushik
%A Amit Jain
%T Malware Detection Techniques in Android
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 17
%P 22-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Mobile Phones have become an important need of today. The term mobile phone and smart phone are almost identical now -a-days. Smartphone market is booming with very high speed. Smartphones have gained such a huge popularity due to wide range of capabilities they offer. Currently android platform is leading the smartphone market. Android has gained an overnight popularity and became the top OS among its competitor OS. This eminence attracted malware authors as well. As android is an open source platform, it seems quite easy for malware authors to fulfill their illicit intentions. In this paper a new technique will be introduced to detect malware. This technique detects malware in android applications through machine learning classifier by using both static and dynamic analysis. This technique does not rely on malware signatures for static analysis but instead android permission model is used. Under dynamic analysis, system call tracing is performed. Using both static and dynamic techniques along with machine learning provides all in one solution for malware detection. The technique used by us is tested on various benign samples collected from official android market (Google Play Store) and on various malicious applications.

  1. IDC data. Available: http://www. idc. com/prodserv/smartphone-os-market-share. jsp
  2. Cabir, Smartphone Malware. Available: http://www. f-secure. com/v-descs/cabir. shtml
  3. Google PlayStore. Available: https://play. google. com/store
  4. Malware Repository, http://contagiominidump. blogspot. com
  5. Thomas Zefferer, Peter Teufl, David Derler, Klaus Potzmader Alexander Oprisnik, Hubert Gasparitz and Andrea Hoeller "Power Consumption-based Application Classification and malware Detection on Android Using Machine-Learning Techniques" in FUTURE COMPUTING 2013
  6. Hahnsang Kim, Joshua Smith, Kang G. Shin "Detecting energy greedy anomalies and mobile malware variants" in MobiSys'08
  7. Aubrey-Derrick Schmidt, Rainer Bye, Hans-Gunther Schmidt, Jan Clausen, Osman Kirazy, Kamer Ali Y¨uksely, Seyit Ahmet Camtepe, and Sahin Albayrak "Static analysis of executables for collaborative malware detection on android" in Communications, 2009. ICC '09. IEEE International Conference
  8. Leonid Batyuk, Markus Herpich, Seyit Ahmet Camtepe, Karsten Raddatz, Aubrey-Derrick Schmidt, and Sahin Albayrak "Using Static Analysis for Automatic Assessment and Mitigation of Unwanted and Malicious Activities Within Android Applications" in Malicious and Unwanted Software (MALWARE), 2011 6th International Conference
  9. Borja Sanz, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, Pablo Garcia Bringas, Gonzalo Álvarez. "PUMA: Permission Usage to Detect Malware in Android", in International Joint Conference CISIS'12-ICEUTE´12-SOCO´12 Special Sessions.
  10. Enck, W. , Gilbert, P. , Chun, B. G. , Cox, L. P. , Jung, J. , McDaniel, P. , Sheth, A. N. : "TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones" in: Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI) (Oct 2010).
  11. Burguera, I. , Zurutuza, U. , & Nadjm-Tehrani, S. (2011). "Crowdroid: Behavior-based malware detection system for Android" in 2011 ACM CCS Workshops on Security and Privacy in Smartphones and Mobile Devices (SPSM'11), 17-21 October 2011, Chicago, Illinois, USA.
  12. Grace, M. , Zhou, Y. , Zhang, Q. , Zou, S. , & Jiang, X. (2012). "RiskRanker: scalable and accurate zero-day Android malware detection. " in The 10th International Conference on Mobile Systems, Applications, and Services (MobiSys'12), Low Wood Bay, Lake District, United Kingdom
  13. Portokalidis, G. , Homburg, P. , Anagnostakis, K. , and Bos, H. : "Paranoid Android: Versatile protection for smartphones" in ACSAC'10, Dec. 2010.
  14. Su, X. , Chuah, M. , Tan, G. "Smartphone dual defense protection framework: Detecting malicious applications in android markets" in: Mobile Ad-hoc and Sensor Networks (MSN), 2012 Eighth International Conference on, pp. 153-160 (2012).
  15. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
  16. Strace, Available: http://en. wikipedia. org/wiki/Strace
Index Terms

Computer Science
Information Sciences


Android Dynamic Analysis Machine learning Malware Malware detection Static Analysis.