Abstract
Malwares have been rising in drastic extent as Android operating system enabled smart phones and tablets getting popularity around the world in last couple of years. For efficient detection of Android malwares, different static and dynamic malware detection methods have been proposed. One of the popular methods of static detection technique is permission/feature-based detection of malwares through AndroidManifest.xml file using machine learning classifiers. But ignoring important feature or keeping irrelevant features may specifically cause mystification for classification algorithms. So to reduce classification time and improvement of accuracy different feature reduction tools have been used in different literature. In this work, we have proposed a framework that extracts the permission features of manifest files, generates feature vectors and uses six different feature ranking tools to create separate feature reducts. On those feature reducts different machine learning classifiers of Data Mining Tool, Weka have been used to classify android applications. We have evaluated our method on a set of total 734 applications (504 benign, 231 malwares) and results show that highest TPR rate observed is 98.01% while accuracy is up to 87.99% and highest F1 score is 0.9189.
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References
I. Burquera, U. Zurutuza, and S. Nadjm-Tehrani: Crowdroid: behavior-based malware detection system for Android. In: 1st ACM workshop on Security and privacy in smartphones and mobile devices, 2011, pp. 15–26, 2011.
W. Xu, F. Zhang, S. Zhu: Permlyzer: Analyzing permission usage in Android applications. In: IEEE International Symposium on Software reliability Engineering (ISSRE), pp. 400–410 (2013).
B. Sanz, I. Santos, X. U. Pedrero, C. Laorden, J. Nieves, P. Garcia Bringas: Instance-based Anomaly Method for Android Malware Detection. SECRYPT, SciTePress, pp. 387–394 (2013).
S.Y. Yerima, S Sezer, G. McWilliams: A new android malware detection using Bayesian classification. In: 27th IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 121–128 (2013).
A. M. Aswini, P. Vinod: Android Malware Analysis Using Ensemble Features. Security, Privacy, and Applied Cryptography Engineering Lecture Notes in Computer Science, vol. 8804, pp. 303–318 (2014).
A. M. Aswini, P. Vinod: Droid Permission Miner: Mining Prominent Permissions for Android Malware Analysis. In: 5th International Conference on the Applications of the Digital Information and Web Technologies (ICADIWAT), pp. 81–86 (2014).
S.Y. Yerima, S Sezer, G. McWilliams, I. Muttik: Analysis of Bayesian classification-based approaches for Android malware detection. IET Information Security, vol. 8, issue 1, pp. 25–36 (2014).
Androguard Project in Google Code Archive, https://code.google.com/p/androguard.
Y. Aafer, W. Du, H. Yin: DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android. Lecture Notes on Security and Privacy in Communication Networks, vol. 127, pp. 86–103, Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (2013).
Z. Aung, W. Zaw: Permission-Based Android Malware Detection. International Journal Of Scientific & Technology Research, vol. 2, issue 3, pp. 228–234 (2013).
S.Y. Yerima, S Sezer, G. McWilliams, I. Muttik: High Accuracy Android malware detection Using Ensemble Learning. IET Information Security, vol. 9, issue 6, pp. 313–320 (2015).
W. Wang, X. Wang, D. Feng, J. Liu, Z. Han, X. Zhang: Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection. IEEE Transactions on Information Forensics and Security, vol. 9, issue 11, pp. 1869–1882 (2014).
K. Allix, T. F. D. A. Bissyande, J. Klein, and Y. Le Traon: Machine Learning-Based Malware Detection for Android Applications: History Matters!. Technical Report, University of Luxembourg, pp. 1–17 (2014).
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Bhattacharya, A., Goswami, R.T. (2017). Comparative Analysis of Different Feature Ranking Techniques in Data Mining-Based Android Malware Detection. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_5
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DOI: https://doi.org/10.1007/978-981-10-3153-3_5
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