Anomaly-Based Insider Threat Detection via Hierarchical Information Fusion
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44213-1_2
Reference22 articles.
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2. Almardeny, Y., Boujnah, N., Cleary, F.: A novel outlier detection method for multivariate data. IEEE Transactions on Knowledge and Data Engineering (2020)
3. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pp. 93–104 (2000)
4. Glasser, J., Lindauer, B.: Bridging the gap: a pragmatic approach to generating insider threat data. In: 2013 IEEE Security and Privacy Workshops, pp. 98–104. IEEE (2013)
5. Hariri, S., Kind, M.C.: Batch and online anomaly detection for scientific applications in a kubernetes environment. In: Proceedings of the 9th Workshop on Scientific Cloud Computing. ScienceCloud’18, Association for Computing Machinery, New York, NY, USA (2018). DOI: https://doi.org/10.1145/3217880.3217883, https://doi.org/10.1145/3217880.3217883D
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