Nowadays, Android Malware is coded so wisely that it has become very difficult to detect them. The static analysis of malicious code is not enough for detection of malware as this malware hides its method call in encrypted form or it can install the method at runtime. The System Calls tracing is an effective dynamic analysis technique for detecting malware as it can analyze the malware at the run time. Moreover, this technique does not require the application code for malware detection. Thus, this can detect that Android malware also which are difficult to detect with static analysis of code. The paper presented the framework of detecting malicious application from 81 malware families by analysis of dynamic feature System Calls Invoked with machine learning algorithms.

System Call,Malicious application detection,malware families

[1] Schmidt, Aubrey-Derrick, Hans-Gunther Schmidt, Jan Clausen, Kamer A. Yuksel, Osman Kiraz, Ahmet Camtepe, and Sahin Albayrak. "Enhancing security of linux-based android devices." In Proceedings of 15th International Linux Kongress, pp. 1-16. 2008.
[2] Kolbitsch, Clemens, Paolo Milani Comparetti, Christopher Kruegel, Engin Kirda, Xiao-yong Zhou, and XiaoFeng Wang. "Effective and Efficient Malware Detection at the End Host." In USENIX security symposium, pp. 351-366. 2009.
[3] Wang, Xinran, Yoon-Chan Jhi, Sencun Zhu, and Peng Liu. "Detecting software theft via system call based birthmarks." In Computer Security Applications Conference, 2009. ACSAC`09. Annual, pp. 149-158. IEEE, 2009.
[4] Lanzi, Andrea, Davide Balzarotti, Christopher Kruegel, Mihai Christodorescu, and Engin Kirda. "Accessminer: using system-centric models for malware protection." In Proceedings of the 17th ACM conference on Computer and communications security, pp. 399-412. ACM, 2010.
[5] Sanz, Borja, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, and Pablo Garcia Bringas. "On the automatic categorisation of android applications." In Consumer Communications and Networking Conference (CCNC), 2012 IEEE, pp. 149-153. IEEE, 2012.
[6] E.Tchakount, P.Dayang .”System calls analysis of malware on android”. International Journal of Science and Technology. Vol. 2 issue 9,2013
[7] Sato, Ryo, Daiki Chiba, and Shigeki Goto. "Detecting Android malware by analyzing manifest files." Proceedings of the Asia-Pacific Advanced Network 36 (2013): 23-31.
[8] Huang, Chun-Ying, Yi-Ting Tsai, and Chung-Han Hsu. "Performance evaluation on permission-based detection for android malware." In Advances in Intelligent Systems and Applications-Volume 2, pp. 111-120. Springer, Berlin, Heidelberg, 2013.
[9] Canfora, Gerardo, Francesco Mercaldo, and Corrado Aaron Visaggio. "A classifier of malicious android applications." In Availability, Reliability and Security (ARES), 2013 Eighth International Conference on, pp. 607-614. IEEE, 2013.
[10] Liu, Xing, and Jiqiang Liu. "A two-layered permission-based Android malware detection scheme." In Mobile cloud computing, services, and engineering (mobilecloud), 2014 2nd ieee international conference on, pp. 142-148. IEEE, 2014.
[11] Jeong, Youn-sik, Hwan-taek Lee, Seong-je Cho, Sangchul Han, and Minkyu Park. "A kernel-based monitoring approach for analyzing malicious behavior on android." In Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 1737-1738. ACM, 2014.
[12] Arp, Daniel, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and C. E. R. T. Siemens. "DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket." In NDSS. 2014.
[13] Kang, Hyunjae, Jae-wook Jang, Aziz Mohaisen, and Huy Kang Kim. " Comparative analysis of classification algorithm in EDM for improving student performance." International Journal of Distributed Sensor Networks (2015).
[14] S.Malik and K. Khatter. "AndroData: A Tool for Static & Dynamic Feature Extraction of Android Apps." International Journal of Applied Engineering Research,Vol. 10, issue 94, 2015.
[15] S.Malik and K. Khatter. "System Call Analysis of Android Malware Families." Indian Journal of Science and Technology,Vol. 9, issue 21 ,2016.
[16] B.R. Patel, "Comparative analysis of classification algorithm in EDM for improving student performance", International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.171-175, 2017.