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References
[1] Chandola, V., Banerjee, A., & Kumar, V. (2009). “Anomaly detection: A survey”. ACM computing surveys (CSUR), 41(3), 15.
[2] Schubert, E., Koos, A., Emrich, T., Züfle, A., Schmid, K. A., & Zimek, A. (2015). A framework for clustering uncertain data. Proceedings of the VLDB Endowment, 8(12), 1976-1979.2010): 10.
[3] Rapid Miner tutorial, https://rapidminer.com/get-started/ [online, last accessed 5th May 2019]
[4] Scikit tutorial, http://scikit-learn.org/stable/documentation.html [online, last accessed 5th May 2019]
[5] Weka, https://www.cs.waikato.ac.nz/ml/index.html [online, last accessed 5th May 2019]
[6] Sokolova M., Japkowicz, S. "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation" AI 2006 Springer Berlin Heidelberg, 1015-21.
[7] Friedman, Jerome H. "Stochastic gradient boosting." Computational Statistics & Data Analysis 38.4 (2002): 367-378
[8] Bovenzi, A., Brancati, F., Russo, S., & Bondavalli, A. (2015). An os-level framework for anomaly detection in complex software systems. IEEE Transactions on Dependable and Secure Computing, 12(3), 366-372.
[9] M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the kdd cup 99 data set,” in Computational Intelligence for Securit and Defense Applications, 2009. CISDA 2009. IEEESymposium on. IEEE, 2009, pp. 1–6
[10] A. Shiravi, H. Shiravi, M. Tavallaee, and A. A. Ghorbani, “Toward developing a systematic approach to generate benchmark data sets for intrusion detection,”computers & security, vol. 31, no. 3, pp. 357–374, 2012.
[11] N. Moustafa and J. Slay, “UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in Military Communications and Information Systems Conference (Mil-CIS), 2015. IEEE, 2015, pp. 1–6.
[12] S. Rosset and A. Inger, “Kdd-cup 99: knowledge discovery in a charitable organization’s donor database.”
[13] G. Creech and J. Hu, “Generation of a new ids test data set: Time to retire the kdd collection,” in Wireless Communications and Networking Conference (WCNC), 2013 IEEE. IEEE, 2013, pp. 4487–4492.
[14] Di Giandomenico, F., and Strigini, L. “Adjudicators for diverse-redundant components”. In Reliable Distributed Systems, 1990. Proceedings., Ninth Symposium on (pp. 114-123). IEEE.
[15] M. Goldstein and S. Uchida. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4):e0152173, 2016.
[16] Saeys, Y., Abeel, T., & Van de Peer, Y. (2008, September). Robust feature selection using ensemble feature selection techniques. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 313-325). Springer, Berlin, Heidelberg.
[17] GitHub Public Repository https://github.com/tommyippoz/RELOAD/releases
[18] L. Zhang, J. Lin, and R. Karim, “Sliding window-based fault detection from high-dimensional data streams”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 47, no. 2, pp. 289–303, 2017.
[19] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: identifying density-based local outliers. In ACM sigmod record, volume 29, pages 93–104. ACM, 2000.
[20] M. Goldstein and A. Dengel. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. 2012.
[21] J. Tang, Z. Chen, A. W.-C. Fu, and D. W. Cheung. Enhancing effectiveness of outlier detections for low density patterns. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 535–548. Springer, 2002.
[22] S. Ramaswamy, R. Rastogi, and K. Shim. Efficient algorithms for mining outliers from large data sets. In ACM Sigmod Record, volume 29, pages 427–438. ACM, 2000.
[23] M. Amer, M. Goldstein, and S. Abdennadher. Enhancing one-class support vector machines for unsupervised anomaly detection. In Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, pages 8–15. ACM, 2013.
[24] H.-P. Kriegel, A. Zimek, et al. Angle-based outlier detection in high-dimensional data. In Proceedings of the 14th ACM SIGKDD Int. Conference on Knowledge discovery and data mining, pages 444–452. ACM, 2008.
[25] F. T. Liu, K. M. Ting, and Z.-H. Zhou. Isolation forest. In Data Mining, 2008. ICDM’08. Eighth IEEE Int. Conference on, pages 413–422. IEEE, 2008
[26] Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. (2001). A robust outlier detection scheme for large data sets. In In 6th Pacific-Asia Conf. on Knowledge Discovery and Data Mining.
[27] Dupont, Laurent, et al. "Continuous anomaly detection based on behavior modeling and heterogeneous information analysis." U.S. Patent Application No. 12/941,849.
[28] Giotis, Kostas, et al. "Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments." Computer Networks 62 (2014): 122-136.
[29] Chiappetta, Marco, Erkay Savas, and Cemal Yilmaz. "Real time detection of cache-based side-channel attacks using hardware performance counters." Applied Soft Computing 49 (2016): 1162-1174.
[30] Breiman, Leo. "Random forests." Machine learning 45.1 (2001): 5-32.
[31] A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava. A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the 2003 SIAM Int. Conference on Data Mining, pages 25{36. SIAM, 2003.
[32] Cotroneo, Domenico, Roberto Natella, and Stefano Rosiello. "A fault correlation approach to detect performance anomalies in Virtual Network Function chains." Software Reliability Engineering (ISSRE), 2017 IEEE 28th International Symposium on. IEEE, 2017.
[33] The paper is currently being published in an international journal. It introduces the concept of anomaly checkers and explores various approaches for voting strategies, albeit for operating at runtime on a target monitor.
[34] Rodriguez, Juan D., Aritz Perez, and Jose A. Lozano. "Sensitivity analysis of k-fold cross validation in prediction error estimation." IEEE transactions on pattern analysis and machine intelligence 32.3 (2010): 569-575.
[35] Falcão, F., Zoppi, T., Silva, C. B. V., Santos, A., Fonseca, B., Ceccarelli, A., & Bondavalli, A. (2019, April). Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 318-327). ACM.
[36] Kaggle – “Intrusion Detection” dataset uploaded by Jinner, https://www.kaggle.com/what0919/intrusion-detection [online, last accessed 5th May 2019]
[37] OpenML portal, https://www.openml.org/ [online, last accessed 5th May 2019]
[38] Zhou, A., Cao, F., Qian, W., & Jin, C. (2008). Tracking clusters in evolving data streams over sliding windows. Knowledge and Information Systems, 15(2), 181-214.
[39] Karimian, S. H., Kelarestaghi, M., & Hashemi, S. (2012, May). I-inclof: improved incremental local outlier detection for data streams. In Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on (pp. 023-028). IEEE.
[40] Zhang, Liangwei, Jing Lin, and Ramin Karim. "Sliding window-based fault detection from high-dimensional data streams." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47.2 (2017): 289-303.
[41] Mouratidis, K., & Papadias, D. (2007). Continuous nearest neighbor queries over sliding windows. IEEE transactions on knowledge and data engineering, 19(6), 789-803.
[42] Ding, Z., & Fei, M. (2013). An anomaly detection approach based on isolation forest algorithm for streaming data using sliding window. IFAC Proceedings Volumes, 46(20), 12-17.
[43] Schubert, E., & Gertz, M. (2017, October). Intrinsic t-stochastic neighbor embedding for visualization and outlier detection. In International Conference on Similarity Search and Applications (pp. 188-203). Springer, Cham.
[44] Martin Ester,Han-peter Kriegel,Jorg Sander, Xiaowei Xu,”A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, 2nd International conference on Knowledge Discovery and Data Mining (KDD-96)
[45] Radovanović, M., Nanopoulos, A., & Ivanović, M. (2014). Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE transactions on knowledge and data engineering, 27(5), 1369-1382.
[46] Janssens JHM, Huszar F, Postma EO, van den Herik HJ (2012) Stochastic outlier selection. Technical report TiCC TR 2012-001, Tilburg University, Tilburg Center for Cognition and Communication, Tilburg, The Netherlands
[47] Vázquez, Félix Iglesias, Tanja Zseby, and Arthur Zimek. "Outlier detection based on low density models." 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018.
[48] Kohonen, T. (1997, June). Exploration of very large databases by self-organizing maps. In Proceedings of International Conference on Neural Networks (ICNN'97) (Vol. 1, pp. PL1-PL6). IEEE.
[49] Hamerly, G., & Elkan, C. (2004). Learning the k in k-means. In Advances in neural information processing systems (pp. 281-288).
[50] Mennatallah Amer and Markus Goldstein. 2012. Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer. In Conference: Proceedings of the 3rd RapidMiner Community Meeting and Conferernce (RCOMM 2012).