Network anomaly detection in time series using distance based outlier detection with cluster density analysis
Authors: Flanagan, K., Fallon, E., Connolly, P. and Awad, A.
Journal: 2017 Internet Technologies and Applications, ITA 2017 - Proceedings of the 7th International Conference
Pages: 116-121
ISBN: 9781509048151
DOI: 10.1109/ITECHA.2017.8101921
Abstract:It is common place in any organizational environment that data stored internally does not necessarily belong to the company storing the data. In such cases, keeping this data secured is of critical importance. If such data is compromised, it can lead to devastating effects on both the public image of the organization and the relations between said company and its business partners. To combat this surge in malicious activity in recent years, research has focused on using anomaly detection techniques to detect possible malicious activity on a network. This paper proposes an evolution of the MCOD (Micro-Clustering Outlier Detection) machine learning algorithm. Designed to implement a time-series approach along with using both distance based outlier detection and cluster density analysis, we analysis the results of this algorithm on real-world data.
Source: Scopus