On the Use of AutoML for Combating Alert Fatigue in Security Operations Centers
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54129-2_36
Reference15 articles.
1. Ahmed, T., Shah, A., Kolla, M., Yellasiri, R.: Reduction of alert fatigue using extended isolation forest. In: 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), vol. 1, pp. 1–5 (2021). https://doi.org/10.1109/FABS52071.2021.9702617
2. Aminanto, M.E., Ban, T., Isawa, R., Takahashi, T., Inoue, D.: Threat alert prioritization using isolation forest and stacked auto encoder with day-forward-chaining analysis. IEEE Access 8, 217977–217986 (2020). https://doi.org/10.1109/ACCESS.2020.3041837
3. Aminanto, M.E., Zhu, L., Ban, T., Isawa, R., Takahashi, T., Inoue, D.: Combating threat-alert fatigue with online anomaly detection using isolation forest. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) Neural Information Processing, pp. 756–765. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-36708-4_62
4. Ban, T., Samuel, N., Takahashi, T., Inoue, D.: Combat security alert fatigue with AI-assisted techniques. In: Cyber Security Experimentation and Test Workshop, pp. 9–16 (2021)
5. Bertino, E., Kantarcioglu, M., Akcora, C.G., Samtani, S., Mittal, S., Gupta, M.: AI for security and security for AI. In: Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy, pp. 333–334 (2021)
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