Journal of Information Security
This survey aims to deliver an extensive and well-constructed overview of using machine learning for the problem of detecting anomalies in streaming datasets. The objective is to provide the effectiveness of using Hoeffding Trees as a machine learning algorithm solution for the problem of detecting anomalies in streaming cyber datasets. In this survey we categorize the existing research works of Hoeffding Trees which can be feasible for this type of study into the following: surveying distributed Hoeffding Trees, surveying ensembles of Hoeffding Trees and surveying existing techniques using Hoeffding Trees for anomaly detection. These categories are referred to as compositions within this paper and were selected based on their relation to streaming data and the flexibility of their techniques for use within different domains of streaming data. We discuss the relevance of how combining the techniques of the proposed research works within these compositions can be used to address the anomaly detection problem in streaming cyber datasets. The goal is to show how a combination of techniques from different compositions can solve a prominent problem, anomaly detection.
Original Publication Citation
Muallem, A., Shetty, S., Pan, J. W., Zhao, J., & Biswal, B. (2017). Hoeffding tree algorithms for anomaly detection in streaming datasets: A survey. Journal of Information Security, 8(4), 339-361. doi:10.4236/jis.2017.84022
Muallem, Asmah; Shetty, Sachin; Pan, Jan W.; Zhao, Juan; and Biswal, Biswajit, "Hoeffding Tree Algorithms for Anomaly Detection in Streaming Datasets: A Survey" (2017). Computational Modeling & Simulation Engineering Faculty Publications. 17.
This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.