References
[1]. Agrawal, S., & Agrawal, J. (2015). Survey on anomaly detection using data mining techniques. Procedia Computer Science, 60, 708-713.
[2]. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31.
[3]. Akouemo, H. N., & Povinelli, R. J. (2016). Probabilistic anomaly detection in natural gas time series data. International Journal of Forecasting, 32(3), 948-956.
[4]. Al-Yaseen, W. L., Othman, Z. A., & Nazri, M. Z. A. (2017). Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Systems with Applications, 67, 296-303.
[5]. Bakar, U. A. B. U. A., Ghayvat, H., Hasanm, S. F., & Mukhopadhyay, S. C. (2016). Activity and anomaly detection in smart home: A survey. In Next Generation Sensors and Systems (pp. 191-220). Springer, Cham.
[6]. Cao, Y., Li, Y., Coleman, S. A., Belatreche, A., & McGinnity, T. M. (2015). Adaptive hidden Markov model with anomaly States for price manipulation detection. IEEE Trans. Neural Netw. Learning Syst., 26(2), 318-330.
[7]. Cheng, K. W., Chen, Y. T., & Fang, W. H. (2015). Video anomaly detection and localization using hierarchical feature representation and Gaussian process regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2909-2917).
[8]. Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2004, July). Extreme learning machine: A new learning scheme of feedforward neural networks. In Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on (Vol. 2, pp. 985-990). IEEE.
[9]. Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neuro- Computing, 70(1-3), 489-501.
[10]. Kadri, F., Harrou, F., Chaabane, S., Sun, Y., & Tahon, C. (2016). Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems. Neuro-Computing, 173, 2102-2114.
[11]. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
[12]. Paula, E. L., Ladeira, M., Carvalho, R. N., & Marzagão, T. (2016, December). Deep learning anomaly detection as support fraud investigation in brazilian exports and anti-money laundering. In Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on (pp. 954-960). IEEE.
[13]. Salami, H. O., Ibrahim, R. S., & Yahaya, M. O. (2016). Detecting anomalies in students' results using decision trees. International Journal of Modern Education and Computer Science, 8(7), 31-40
[14]. Singh, R., Kumar, H., & Singla, R. K. (2015). An intrusion detection system using network traffic profiling and online sequential extreme learning machine. Expert Systems with Applications, 42(22), 8609-8624.
[15]. Vallis, O., Hochenbaum, J., & Kejariwal, A. (2014, June). A novel technique for long-term anomaly detection in the cloud. In HotCloud.
[16]. Wang, Y., Li, D., Du, Y., & Pan, Z. (2015). Anomaly detection in traffic using L1-norm minimization extreme learning machine. Neuro-Computing, 149, 415-425.
[17]. Xiang, J., Westerlund, M., Sovilj, D., & Pulkkis, G. (2014, November). Using extreme learning machine for intrusion detection in a big data environment. In Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (pp. 73-82). ACM.