Abstract
In a large monitored information system, analysts are confronted with a huge number of heterogeneous events or alerts produced by audit mechanisms or Intrusion Detection Systems. Even though they can use SIEM software to collect and analyse these eventsĀ (In this paper we call events all events or alerts produced by the monitoring processes), detecting previously unknown threats is tedious. Event prioritization tools can help the analyst focus on potentially anomalous events. To compute a measure of priority among events, we propose in this paper to define the notion of an anomaly score for each attribute of the analyzed events and a method for regrouping events in clusters to reduce the number of alerts the analysts have to qualify. The anomaly score is computed using neural networks (i.e., auto-encoders) trained on a normal dataset of events, and then used to provide the analyst with the information of the difference between normal learned events and the events actually produced by the monitoring system. Additionally, the auto-encoders also provide a way to regroup similar events via clustering.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Attributes are the fields of an event. Connection duration, source IP address, number of bytes received are examples of attributes for a network event.
References
Akoglu, L., Tong, H., Vreeken, J., Faloutsos, C.: Fast and reliable anomaly detection in categorical data. In: Proceedings of the 21st ACM international conference on Information and Knowledge Management, pp. 415ā424. ACM (2012)
Bellman, R.E., Dreyfus, S.E.: Applied Dynamic Programming. Princeton University Press, Princeton (2015)
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. 93ā104 (2000)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1ā22 (1977)
Dilokthanakul, N., et al.: Deep unsupervised clustering with Gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648 (2016)
Ding, Z., Fei, M.: An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window. IFAC Proc. Volumes 46(20), 12ā17 (2013)
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, pp. 1285ā1298. ACM (2017)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96, 226ā231 (1996)
Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170ā180. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46145-0_17
He, Z., Xu, X., Huang, J.Z., Deng, S.: A frequent pattern discovery method for outlier detection. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 726ā732. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-27772-9_80
Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3), 215ā236 (1996)
Kriegel, H.P., S hubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 08, p. 444. ACM Press (2008). https://doi.org/10.1145/1401890.1401946
Lanoe, D., Hurfin, M., Totel, E.: A scalable and efficient correlation engine to detect multi-step attacks in distributed systems. In: 2018 IEEE 37th Symposium on Reliable Distributed Systems (SRDS), pp. 31ā40, October 2018. https://doi.org/10.1109/SRDS.2018.00014
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413ā422. IEEE (2008)
Liu, F., Wen, Y., Zhang, D., Jiang, X., Xing, X., Meng, D.: Log2vec: a heterogeneous graph embedding based approach for detecting cyber threats within enterprise. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1777ā1794 (2019)
Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)
Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint arXiv:1802.09089 (2018)
Pang, G., Ting, K.M., Albrecht, D.: Lesinn: detecting anomalies by identifying least similar nearest neighbours. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 623ā630. IEEE, November 2015. https://doi.org/10.1109/ICDMW.2015.62
Pascoal, C., De Oliveira, M.R., Valadas, R., Filzmoser, P., Salvador, P., Pacheco, A.: Robust feature selection and robust pca for internet traffic anomaly detection. In: 2012 Proceedings IEEE Infocom, pp. 1755ā1763. IEEE (2012)
Paxson, V.: Bro: a system for detecting network intruders in real-time. Comput. Netw. 31(23ā24), 2435ā2463 (1999)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier, Amsterdam (2014)
Schƶlkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C.: Support vector method for novelty detection. In: Advances in Neural Information Processing Systems, pp. 582ā588 (2000)
Shen, Y., Stringhini, G.: Attack2vec: leveraging temporal word embeddings to understand the evolution of cyberattacks. In: 28th \(\{\)USENIX\(\}\) Security Symposium Security 2019, pp. 905ā921 (2019)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998ā6008 (2017)
Veeramachaneni, K., Arnaldo, I., Korrapati, V., Bassias, C., Li, K.: Ai\({\hat{\,}}\) 2: training a big data machine to defend. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), pp. 49ā54. IEEE (2016)
Wong, W.K., Moore, A.W., Cooper, G.F., Wagner, M.M.: Bayesian network anomaly pattern detection for disease outbreaks. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 808ā815 (2003)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665ā674. ACM (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Ā© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Dey, A., Totel, E., Navers, S. (2021). Heterogeneous Security Events Prioritization Using Auto-encoders. In: Garcia-Alfaro, J., Leneutre, J., Cuppens, N., Yaich, R. (eds) Risks and Security of Internet and Systems. CRiSIS 2020. Lecture Notes in Computer Science(), vol 12528. Springer, Cham. https://doi.org/10.1007/978-3-030-68887-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-68887-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68886-8
Online ISBN: 978-3-030-68887-5
eBook Packages: Computer ScienceComputer Science (R0)