Lov Kumar
Chinmay Hota
ABSTRACT
Android is currently the most popular smartphone platform which occupied 88% of global sale by the end of 2nd quarter 2018. With the popularity of these applications, it is also inviting cybercriminals to develop malware application for accessing important information from smartphones. The major objective of cybercriminals to develop Malware apps or Malicious apps to threaten the organization privacy data, user privacy data, and device integrity. Early identification of such malware apps can help the android user to save private data and device integrity. In this study, features extracted from intermediate code representations obtained using decompilation of APK file are used for providing requisite input data to develop the models for predicting android malware applications. These models are trained using extreme learning with multiple kernel functions ans also compared with the model trained using most frequently used classifiers like linear regression, decision tree, polynomial regression, and logistic regression. This paper also focuses on the effectiveness of data sampling techniques for balancing data and feature selection methods for selecting right sets of significant uncorrelated metrics. The high-value of accuracy and AUC confirm the predicting capability of data sampling, sets of metrics, and training algorithms to malware and normal applications.
- ARORA, A., AND PEDDOJU, S. K. Minimizing network traffic features for android mobile malware detection. In Proceedings of the 18th International Conference on Distributed Computing and Networking (2017), ACM, p. 32. Google Scholar
Digital Library
- ARORA, A., AND PEDDOJU, S. K. Ntpdroid: A hybrid android malware detector using network traffic and system permissions. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (2018), IEEE, pp. 808--813.Google ScholarCross Ref
- CHIDAMBER, S. R., AND KEMERER, C. F. Towards a metrics suite for object oriented design. Google ScholarDigital Library
- CHIDAMBER, S. R., AND KEMERER, C. F. A metrics suite for object oriented design. IEEE Transactions on software engineering 20, 6 (1994), 476--493. Google ScholarDigital Library
- DEMONTIS, A., MELIS, M., BIGGIO, B., MAIORCA, D., ARP, D., RIECK, K., CORONA, I., GIACINTO, G., AND ROLI, F. Yes, machine learning can be more secure! a case study on android malware detection. IEEE Transactions on Dependable and Secure Computing (2017). Google ScholarDigital Library
- HUANG, G.-B., ZHU, Q.-Y., AND SIEW, C.-K. Extreme learning machine: theory and applications. Neurocomputing 70, 1-3 (2006), 489--501.Google ScholarCross Ref
- KADIR, A. F. A., STAKHANOVA, N., AND GHORBANI, A. A. Android botnets: What urls are telling us. In International Conference on Network and System Security (2015), Springer, pp. 78--91.Google ScholarCross Ref
- KUMAR, D. R., AND CHANAKYA, G. Refactoring framework for instance code smell detection. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 3, 9 (2014).Google Scholar
- KUMAR, L., SATAPATHY, S. M., AND KRISHNA, A. Application of smote and lssvm with various kernels for predicting refactoring at method level. In International Conference on Neural Information Processing (2018), Springer, pp. 150--161.Google ScholarDigital Library
- KUMAR, L., SRIPADA, S. K., SUREKA, A., AND RATH, S. K. Effective fault prediction model developed using least square support vector machine (lssvm). Journal of Systems and Software 137 (2018), 686--712.Google ScholarCross Ref
- KUMAR, M., MEENA, J., SINGH, R., AND VARDHAN, M. Data outsourcing: A threat to confidentiality, integrity, and availability. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) (2015), IEEE, pp. 1496--1501. Google ScholarDigital Library
- KUMAR, R., SINGH, J., AND KAUR, A. An empirical study of bad smell in code on maintenance effort. Int. J. Comput. Sci. Eng 5 (2016), 294--306.Google Scholar
- LI, W., AND HENRY, S. Maintenance metrics for the object oriented paradigm. In {1993} Proceedings First International Software Metrics Symposium (1993), IEEE, pp. 52--60.Google Scholar
- MAHINDRU, A., AND SINGH, P. Dynamic permissions based android malware detection using machine learning techniques. In Proceedings of the 10th Innovations in Software Engineering Conference (2017), ACM, pp. 202--210. Google ScholarDigital Library
- MCLAUGHLIN, N., MARTINEZ DEL RINCON, J., KANG, B., YERIMA, S., MILLER, P., SEZER, S., SAFAEI, Y., TRICKEL, E., ZHAO, Z., DOUPÉ, A., ET AL. Deep android malware detection. In Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy (2017), ACM, pp. 301--308. Google ScholarDigital Library
- ROJAS, J. A. R., KERY, M. B., ROSENTHAL, S., AND DEY, A. Sampling techniques to improve big data exploration. In 2017 IEEE 7th symposium on large data analysis and visualization (LDAV) (2017), IEEE, pp. 26--35.Google ScholarCross Ref
- SHEEN, S., ANITHA, R., AND NATARAJAN, V. Android based malware detection using a multifeature collaborative decision fusion approach. Neurocomputing 151 (2015), 905--912.Google ScholarCross Ref
- ZHOU, Y., AND JIANG, X. Dissecting android malware: Characterization and evolution. In 2012 IEEE symposium on security and privacy (2012), IEEE, pp. 95--109. Google ScholarDigital Library
Index Terms
- Android Malware Prediction Using Extreme Learning Machine with Different Kernel Functions
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