A Log-Based Method to Detect and Resolve Efficiently Conflicts in Access Control Policies
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-73689-7_79
Reference9 articles.
1. Ait El Hadj, M., Khoumsi, A., Benkaouz, Y., Erradi, M.: Efficient security policy management using suspicious rules through access log analysis. In: International Conference on Networked Systems, pp. 250–266. Springer (2019)
2. Du, M., Li, F., Zheng, G., Srikumar, V.: DeepLog: anomaly detection and diagnosis from system logs through deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM (2017)
3. Lin, Q., Zhang, H., Lou, J.-G., Zhang, Y., Chen, X.: Log clustering based problem identification for online service systems. In: Proceedings of the 38th International Conference on Software Engineering Companion, pp. 102–111. ACM (2016)
4. Ait El Hadj, M., Khoumsi, A., Benkaouz, Y., Erradi, M.: Formal approach to detect and resolve anomalies while clustering ABAC policies. EAI Endorsed Trans. Secur. Saf. 5(16) (2018)
5. Nguyen, Q.P., Lim, K.W., Divakaran, D.M., Low, K.H., Chan, M.C.: GEE: a gradient-based explainable variational autoencoder for network anomaly detection. In: 2019 IEEE Conference on Communications and Network Security (CNS), pp. 91–99. IEEE (2019)
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