Towards fuzzy anomaly detection-based security: a comprehensive review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Logic,Software
Link
https://link.springer.com/content/pdf/10.1007/s10700-020-09332-x.pdf
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3. Aljawarneh, S. A., Radhakrishna, V., & Kumar, G. R. (2017). A fuzzy measure for intrusion and anomaly detection. In 2017 International conference on engineering and MIS (ICEMIS) (pp. 1–6).
4. Aminanto, M. E., Kim, H., Kim, K.-M., & Kim, K. (2017). Another fuzzy anomaly detection system based on ant clustering algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 100, 176–183.
5. Aparicio-Navarro, F. J., Kyriakopoulos, K. G., Parish, D. J., & Chambers, J. A. (2016). Adding contextual information to intrusion detection systems using fuzzy cognitive maps. In 2016 IEEE International multi-disciplinary conference on cognitive methods in situation awareness and decision support (CogSIMA) (pp. 180–186).
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