DATA MINING BASED MALICIOUS APPLICATION DETECTION OF ANDROID
Keywords:Antenna Data mining, malicious application, android mobile, detection.
One of the most popular mobile phone platforms is an Android mobile phone platform owned by Google. The Android platform is open source to allow the developers to develop the full future application of the mobile operating system. Nowadays, malicious applications have been expanding in scale as an Android system. In this paper a data mining aided approach to detect malware applications in Android applications is presented. This approach capture the instant attracts that cannot be conclusively identified in past work. Static detection is one of the popular methods based on permissions detection of maliciousness in all the way through AndroidManifest.xml by classifiers. This paper suggests implementing a malicious application identify tool called Androidspy. Initially observe the relationship among system functions, sensitive permissions, and interface of responsive programming. Then, examine the system function grouping that has been clarifying the application behavior and characteristic vector. Following on the characteristic vectors, finding malicious android applications used to be nae Bayesian, function decision algorithm, methodologies of j48decision tree. Androidspy is real-world applications as well as test sample programs. The test sample result confirms that Androidspy can be enhanced to detect malicious applications by using the system function group estimated with the previous work.
2. Milosevic, Nikola, Ali Dehghantanha, and Kim-Kwang Raymond Choo. "Machine learning aided Android malware classification." Computers & Electrical Engineering 61 (2017): 266-274.
3. Li, Jin, Lichao Sun, Qiben Yan, Zhiqiang Li, Witawas Srisa-an, and Heng Ye. "Significant permission identification for machine-learning-based android malware detection." IEEE Transactions on Industrial Informatics 14, no. 7 (2018): 3216-3225.
4. Shabtai, Asaf, Yuval Fledel, and Yuval Elovici. "Automated static code analysis for classifying android applications using machine learning." In 2010 International Conference on Computational Intelligence and Security, pp. 329-333. IEEE, 2010.
5. Amos, Brandon, Hamilton Turner, and Jules White. "Applying machine learning classifiers to dynamic android malware detection at scale." In 2013 9th international wireless communications and mobile computing conference (IWCMC), pp. 1666-1671. IEEE, 2013.
6. Wu, Wen-Chieh, and Shih-Hao Hung. "DroidDolphin: a dynamic Android malware detection framework using big data and machine learning." In Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems, pp. 247-252. ACM, 2014.
7. Chen, Sen, Minhui Xue, Zhushou Tang, Lihua Xu, and Haojin Zhu. "Stormdroid: A streaminglized machine learning-based system for detecting android malware." In Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, pp. 377-388. ACM, 2016.
8. Ham, Hyo-Sik, and Mi-Jung Choi. "Analysis of android malware detection performance using machine learning classifiers." In 2013 international conference on ICT Convergence (ICTC), pp. 490-495. IEEE, 2013.
9. Wei, Linfeng, Weiqi Luo, Jian Weng, Yanjun Zhong, Xiaoqian Zhang, and Zheng Yan. "Machine learning-based malicious application detection of android." IEEE Access 5 (2017): 25591-25601.
10. Pandiaraj, P. "Efficient Architecture of Combined Radix Dif Algorithm for MIMO-OFDM Application." International Journal of Advances In Signal And Image Sciences 2, No. 2 (2016): 9-13.
11. Amjath Ali, J., B. Thangalakshmi, and A. Vincy Beaulah. "IoT Based Disaster Detection and Early Warning Device." International Journal of MC Square Scientific Research (IJMSR) 9, no. 3 (2017): 20-25.
12. Prakash, Gyan, Nishant Saurav, and Venkata Reddy Kethu. "An Effective Undesired Content Filtration and Predictions Framework in Online Social Network." International Journal of Advances in Signal and Image Sciences 2, no. 2 (2016): 1-8.
13. Prakash, Gyan, Nishant Saurav, and Venkata Reddy Kethu. "An Effective Undesired Content Filtration and Predictions Framework in Online Social Network." International Journal of Advances in Signal and Image Sciences 2, no. 2 (2016): 1-8.
Authors need to sign following agreement with International Journal of MC Square Scientific Research before publishing their articles:
- Authors need to return copyright form to Journal Editor-in-chief to proceed their articles for publication. Meantime, the journal licensed under a Creative Commons Attribution License, which permits other user to distribute the work with an acknowledgement of the authors for International Journal of MC Square Scientific Research.
- Authors are also able to share their separate, additional contractual arrangements for the non-restricted contribution of the journal with an acknowledgement of publication in International Journal of MC Square Scientific Research.
- Authors are allowed and encouraged to share their work during the submission process for increasing citation and exploring to increase the paper availability in worldwide way. The Effect of Open Access.