References
Burnap, P., French, R., Turner, F. & Jones, K. (2018). Malware classification using self 859 organizing feature maps and machine activity data. Computer Security, 73, 399–410. Elhadi, A. A. E., Maarof, M. A. & Barry, B. I. (2013). Improving the detection of malware be- 874 haviour using simplified data dependent API call graph. International Journal Security Application, 7 (5), 875 29–42. Eslam, A. & Ivan, Z. (2018). A dynamic Windows malware detection and prediction method based on contextual understanding of API call sequence. Computers & Security, 30(40), 1-15. Gandotra, E., Bansal, D. & Sofat, S. (2014). Malware analysis and classification: a survey. 885 Journal of Information Security, 5 (02), 56. Gibert, D., Mateu, C. & Planes, J. (2020). The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. Journal of Network and Computer Applications, 153(2020), 1-22, 2020. Karbab, E. B., Debbabi, M., Derhab, A. & Mouheb, D. (2018). MalDozer: Automatic framework for android malware detection using deep learning, Digital Investigation 24, 548-559. Kim, T., Kang, B., Rho, M., Sezer, S. & Gyu, E. (2019). A Multimodal Deep Learning Method for Android Malware Detection using Various Features, in IEEE Transactions on Information Forensic and Security, 10(3), 773-778. Li, J., Sunk, L., Yan, Q., Zhiqiang, L. Srisaan, W. & Heng, Y. (2018). “Significant Permission Identification for Machine Learning Based Android Malware Detection”, in IEEE Transactions on Industrial Informatics, 14(7), 3216-3225. Mario, L., Marta, C., Damiano, D., Fabio, M. & Francesco, M. (2019). Dynamic malware detection and phylogeny analysis using process mining. International Journal of Information Security, 18, 257–284. McLaughlin, N. Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., Safaei, Y., Trickel, E., Zhao, Z., Doupe, A, & Ahn, G. (2017). Deep Android Malware Detection, Proceeding on the Seventh ACM on Conference on Data and Application Security and Privacy, 301-308. Nighat, U., Saeeda, U., Fazlullah, K., Mian, A., Ahthasham S., Mamoun A., Paul W. (2021). Intelligent Dynamic Malware Detection using Machine Learning in IP Reputation for Forensics Data Analytics. Future Generation Computer Systems118 (2021), 124–141. Pengbin, F., Jianfeng M., Cong S., Xinpeng X. & Yuwan M. (2018). A Novel Dynamic Android Malware Detection System with Ensemble Learning. IEEE Access, 6, 30996-31011. Qiao, Y., Yang, Y., He, J., Tang, C. & Liu, Z. (2014). CBM: free, automatic malware anal- 923 ysis framework using API call sequences. In: Knowledge Engineering and Man- 924 agreement. Springer, Berlin, Heidelberg, 225–236. Rieck, K., Holz, T., Willems, C., Dussel, P. & Laskov, P. (2008). Learning and classification of malware behavior, in DIMVA ’08: Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment. Berlin, Heidelberg: Springer-Verlag, 108–125. Souri, A. & Hosseini, R. (2018). A state-of-the-art survey of malware detection approaches using data mining techniques, Human. Centric. Computing and Information Sciences, 1-22. Vinayakumar, A., Alazab, M., Soman, M., Poornachandran, P. & Venkatraman, S. (2019). “Robust Intelligent Malware Detection Using Deep Learning” In IEEE Access, 7, 46717-46738. Vinayakumar, M., Alazab, K., Soman, P. & Poornachandran, S. (2019). Venkatraman “Robust Intelligent Malware Detection Using Deep Learning” In IEEE Access, (7), 46717-46738. Yanfang, Y. (2017). A Survey on Malware Detection Using Data Mining Techniques, ACM Computing Surveys, 50.
