Author:
Brabec Jan,Komárek Tomáš,Franc Vojtěch,Machlica Lukáš
Publisher
Springer International Publishing
Reference21 articles.
1. Axelsson, S., Sands, D.: The base-rate fallacy and the difficulty of intrusion detection. Understanding Intrusion Detection Through Visualization, pp. 31–47 (2006)
2. Brabec, J., Machlica, L.: Bad practices in evaluation methodology relevant to class-imbalanced problems. Critiquing and correcting trends in machine learning workshop at NeurIPS abs/1812.01388 (2018). http://arxiv.org/abs/1812.01388
3. Brabec, J., Machlica, L.: Decision-forest voting scheme for classification of rare classes in network intrusion detection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3325–3330, October 2018. https://doi.org/10.1109/SMC.2018.00563
4. Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 875–886. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-09823-4_45
5. Damodaran, A., Di Troia, F., Visaggio, C.A., Austin, T.H., Stamp, M.: A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hacking Tech. 13(1), 1–12 (2015). https://doi.org/10.1007/s11416-015-0261-z
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