1 |
Aragorn, T., YunChun, C., YiHsiang, K., and Tsungnan, L. (2016). Deep Learning for Ransomware Detection, IEICE Technical Report, 116, 87-92.
|
2 |
Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory, John Wiley & Sons, New York.
|
3 |
Huh, M. Y. and Choi, B. S. (2009). Variable selection based on mutual information, Communications of the Korean Statistical Society, 16, 143-155.
|
4 |
Moser, A., Kruegel, C., and Kirda, E. (2007). Limits of Static Analysis for Malware Detection, 23rd Annual Computer Security Applications Conference.
|
5 |
Kim, J., Ji, S., and Kim, S. (2017a). A machine learning based ransomware detection model using a hybrid analysis, Journal of Security Engineering, 14, 263-280.
DOI
|
6 |
Kim, J. H., Park, K. S., and Park, Y. H. (2017b). A study of vulnerability analysis of ransomware detection techniques, The Korean Institute of Communications and Information Sciences 2017 Summer Conference, 590-591.
|
7 |
Lee, H., Seong, J., Kim, Y., Kim, J., and Gim, G. (2017). The automation model of ransomware analysis and detection pattern, Journal of the Korea Institute of Information and Communication Engineering, 21, 1581-1588.
|
8 |
O'Gorman, G. and McDonald, G. (2012). Ransomware: a growing menace, Symantec Security Response.
|
9 |
Sgandurra, D., Munoz-Gonzalez, L., Mohsen, R., and Lupu, E. C. (2016). Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection. arXiv preprint arXiv:1609.03020.
|
10 |
Zhang, H., Xiao, X., Mercaldo, F., Ni, S., Martinelli, F., Sangaiah, A., K.(2019). Classification of ransomware families with machine learning based on N-gram of opcodes, Future Generation Computer Systems, 90, 211-221.
DOI
|