Abstract
In the current era of digital communications, cyber security and data protection have always been a top priority. More and more organizations are starting to take insider threat security seriously. Traditional rule-based anomaly detection solutions generate a large number of alerts and are difficult to adapt to particular scenarios. UEBA (User and Entity Behavior Analysis), which correlates entities, events, and users, has become an emerging organizational solution by combining all-around contextual analysis through statistical and machine learning methods. In this paper, we propose MUEBA, a multi-model UEBA system for spatiotemporal analysis, combining user individual historical analysis and group analysis to detect insider threats. The individual historical analysis module uses the attention-based LSTM to improve the model’s sensitivity to abnormal operations and help security analysts improve their efficiency in responding to threat events. In the group analysis module, we have extended the iForest algorithm in attribute selection and iTree construction, which increased the algorithm’s stability. Finally, we comprehensively decide on the above two aspects and propose a full-time user and entity behavior analysis system. Experimental evaluations on the public CERT-4.2 dataset show that our system outperforms either single model in both stability and precision.
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References
Daniel C., Michael A., Matthew C., Samuel P., George S., Derrick S.: An Insider Threat Indicator Ontology. Technical Report CMU/SEI-2016-TR-007. Software Engineering Institute, Carnegie Mellon University, Pittsburgh (2016)
CSO, CERT Division of SRI-CMU, and Force Point. 2018 U.S. State of Cybercrime. Technical Report (2018)
Shuhan, Y.: Deep learning for insider threat detection: review, challenges and opportunities. Comput. Secur. 104, 102221 (2021). https://doi.org/10.1016/j.cose.2021.102221
Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds.): ICAIS 2021. LNCS, vol. 12737. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78612-0
Lavanya, P., Shankar Sriram, V.S.: Detection of insider threats using deep learning: a review. In: Nayak, J., Behera, H., Naik, B., Vimal, S., Pelusi, D. (eds.) Computational Intelligence in Data Mining. Smart Innovation, Systems and Technologies, Vol 281. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-9447-9_4
Gorka S., Avivah L., Toby B., Tricia P.: Market guide for user and entity behavior analytics, Gartner inc. (2018)
Kim, J., Park, M., Kim, H., Cho, S., Kang, P.: Insider threat detection based on user behavior modeling and anomaly detection algorithms. Appl. Sci. 9(19), 4018 (2019). https://doi.org/10.3390/app9194018
Emmanuel CandÃ\(\acute{\text{l}}\)s, J., Li, X., Ma, Y., John W.: Robust principal component analysis? J. ACM 58(3), 37 (2011). https://doi.org/10.1145/1970392.1970395
Heller, K., Svore, K., Keromytis, A., Stolfo S.: One class support vector machines for detecting anomalous windows registry accesses. In: ICDM Workshop on Data Mining for Computer Security, Melbourne, FL, (2003). https://doi.org/10.7916/D84B39Q0
Fei, T.L., Kai, M.T., Zhihua, Z.: Isolation Forest. In: Eighth IEEE International Conference Data Mining, vol. 2008, pp. 413–422 (2008). https://doi.org/10.1109/ICDM.2008.17
Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: 2000. LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, New York, NY, USA, pp. 93–104. https://doi.org/10.1145/335191.335388
Madhu, S., Minyi, S., Jisheng, W.: User and entity behavior analytics for enterprise security. In: IEEE International Conference on Big Data (Big Data), pp. 1867–1874 (2016). https://doi.org/10.1109/BigData.2016.7840805
Haidar, D., Gaber, M. M.: Adaptive one-class ensemble-based anomaly detection: an application to insider threats. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–9 (2018). https://doi.org/10.1109/IJCNN.2018.8489107
Yilin, W., Yun, Z., Cheng, Z., Xianqiang, Z., Weiming, Z.: Abnormal behavior analysis in office automation system within organizations. Int. J. Comput. Commun. Eng. 6, 212–220 (2017). https://doi.org/10.17706/IJCCE.2017.6.3.212-220
Pankaj, M., Lovekesh, V., Gautam, S., Puneet A.: Long short term memory networks for anomaly detection in time series. In: ESANN (2015)
Bontemps, L., Cao, V.L., McDermott, J., Le-Khac, N.-A.: Collective anomaly detection based on long short-term memory recurrent neural networks. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) FDSE 2016. LNCS, vol. 10018, pp. 141–152. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48057-2_9
Sharma, B., Pokharel, P., Joshi, B.: User behavior analytics for anomaly detection using LSTM autoencoder - Insider Threat Detection. In: Porkaew, K., Chignell, M.H., Fong, S., Watanapa, B. (eds.) IAIT, pp. 5:1–5:9. ACM. https://doi.org/10.1145/3406601.3406610
Xiangyu, X., et al.: An ensemble approach for detecting anomalous user behaviors. Int. J. Softw. Eng. Knowl. Eng. 28(11–12), 1637–1656 (2018). https://doi.org/10.1142/S0218194018400211
Sun, D., Liu, M., Li, M., Shi, Z., Liu, P., Wang, X.: DeepMIT: a novel malicious insider threat detection framework based on recurrent neural network. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 335–341 (2021). https://doi.org/10.1109/CSCWD49262.2021.9437887
Brown, A., Tuor, A., Hutchinson, B., Nichols, N.: Recurrent neural network attention mechanisms for interpretable system log anomaly detection. CoRR, abs/1803.04967 (2018). https://doi.org/10.1145/3217871.3217872
Benchaji, I., Douzi, S., El Ouahidi, B., Jaafari, J.: Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J. Big Data 8(1), 1–21 (2021). https://doi.org/10.1186/s40537-021-00541-8
Xia, L., Li, Z.: A new method of abnormal behavior detection using LSTM network with temporal attention mechanism. J. Supercomput. 77(4), 3223–3241 (2020). https://doi.org/10.1007/s11227-020-03391-y
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Liu, J., Zhang, J., Du, C., Wang, D. (2023). MUEBA: A Multi-model System for Insider Threat Detection. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13655. Springer, Cham. https://doi.org/10.1007/978-3-031-20096-0_23
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