MiMaLo: Advanced Normalization Method for Mobile Malware Detection
Автор: Sriyanto, Sahib B. Sahrin, Abdullah Mohd. Faizal, Nanna Suryana, Adang Suhendra
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 5 vol.14, 2022 года.
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A range of research procedures have been executed to overcome malware attacks. This research used a malware behavior observe approach on device calls on mobile devices operating gadget kernel. An application used to be mounted on mobile gadget to gather facts and processed them to get dataset. This research used data mining classification approach method and validates it using ten fold cross validation. MiMaLo is a method to normalize a dataset the usage of the min-max aggregate and logarithm function. The application of the MiMaLo method aims to increase the accuracy value. Derived from the experiments, the classifiers overall performance level used to be extensively increasing. The application of the MiMaLo method using the neural network algorithm produces an accuracy of 93.54% with AUC of 0.982.
Malware Attack, Mobile Malware Detection, Normalization Methods, MiMaLo
Короткий адрес: https://sciup.org/15019086
IDR: 15019086 | DOI: 10.5815/ijmecs.2022.05.03
Список литературы MiMaLo: Advanced Normalization Method for Mobile Malware Detection
- Dimjasevic, M., Atzeni, S., Ugrina, I., & Rakamaric, Z. 2015. Android Malware Detection Based on System Calls. UUCS-15-003, 11(1), 209–216
- Comput, J.P.D., Tong, F., and Yan, Z., 2017. A Hybrid Approach Of Mobile Malware Detection In Android. J. Parallel Distrib. Comput., 103, pp.22–31.
- T.Bell.1999. The Concept of Dynamic Analysis. ACM SIGSOFT Softw. Eng. Notes.24, 6 (1999), 216-234.
- Lin, C.H., Pao, H.K., and Liao, J.W., 2018. Efficient Dynamic Malware Analysis Using Virtual Time Control Mechanics. Computers and Security, 73, pp.359–373.
- Or-Meir, O., Nissim, N., Elovici, Y., Rokach, L. 2019. Dynamic Malware Analysis in the Modern Era-A State of the Art Survey. ACM Computing Surveys, Vol.52, No.5, Articles 88. September 2019.
- Abela, K. J., Alas, J. R. D., Angeles, D. K., Tolentino, R. J., and Gomez, M. A., 2013. Automated Malware Detection for Android AMDA. In The Second International Conference on Cyber Security, Cyber Peacefare and Digital Forensic (CyberSec2013) pp. 180-188.
- Seo, S.-H., Gupta, A., Mohamed Sallam, A., Bertino, E., and Yim, K., 2014. Detecting Mobile Malware Threats To Homeland Security Through Static Analysis. Journal of Network and Computer Applications, 38, pp.43–53.
- Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., and Rieck, K. 2014. Drebin: Efficient and Explainable Detection of Android Malware in Your Pocket. Proceeding of 17th Network and Distributed System Security Symposium (NDSS).
- Kabakus, A.T. and Dogru, I.A., 2018. An In-Depth Analysis Of Android Malware Using Hybrid Techniques. Digital Investigation, 24, pp.25–33.
- Faruki, P., Ganmoor, V., Laxmi, V., Gaur, M. S., and Bharmal, A., 2013. AndroSimilar: robust statistical feature signature for Android malware detection. In Proceedings of the 6th International Conference on Security of Information and Networks ACM, pp. 152-159.
- Lin, C.H., Pao, H. K. and Liao, J.W., 2018. Efficient dynamic malware analysis using virtual time control mechanics. Computers and Security, 73, pp. 359–373.
- Dini, G., Martinelli, F., Saracino, A., and Sgandurra, D. 2013. Probabilistic Contract Compliance for Mobile Applications. In Availability, Reliability and Security (ARES), 2013 Eighth International Conference on IEEE, pp. 599-606.
- Enck, W., Gilbert, P., Chun, B. G., Cox, L. P., Jung, J., McDaniel, P., and Sheth, A., 2010. TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones. In OSDI, 10, pp. 1-6.
- Xu, R., Saïdi, H. and Anderson, R., 2012. Aurasium: Practical policy enforcement for android applications. In Proceedings of the 21st USENIX conference on Security symposium. pp. 27-27.
- Wei, T. E., Mao, C. H., Jeng, A. B., Lee, H. M., Wang, H. T. and Wu, D. J., 2012. Android Malware Detection via a Latent Network Behavior Analysis. In Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference, pp. 1251-1258.
- Sanz B., Santos I., Ugarte-P. X., Laorden C., Nieves J. and Bringas P. G., 2013. Instance-based Anomaly Method for Android Malware Detection. In Proceedings of the 10th International Conference on Security and Cryptography (SECRYPT), pp. 387-394
- Jiawei, H., Kamber, M., Han, J., Kamber, M. and Pei, J., 2012. Data Mining: Concepts and Techniques. San Francisco, CA, itd: Morgan Kaufmann.
- Bebu, I., Luta, G., Mathew, T., Agan, K, B,. 2016. Generalized Confidence Intervals and Fiducial Intervals for Some Epidemiological Measures. International Journal of Environmental Research and Public Health. MPDI. 13 , 605. doi : 10.3390/ijerph13060605
