Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set

Abstract

IntroductionAnomaly detection for analysing spatio-temporal data remains a rapidly growing problem in the wake of an ever-increasing number of advanced sensors that are continuously generating large-scale datasets. For example, vehicle GPS tracking, social media, financial network and router logs, and high resolution surveillance cameras all generate a huge amount of spatio-temporal data. This technology is also important in the context of cyber security since cyber data carries with it an IP address which can map to a specific geolocation and a timestamp. Yet, current cybersecurity approaches are not able to process this kind of information effectively. To illustrate this deficiency, consider the scenario of a distributed denial-of-service (DDoS) attack in which the network packets may come from different IP addresses with sparse locations. In such a case, a spatio-temporal analyzing system [1] is required to analyse the spatial pattern of the DDoS attack. Yet, user oriented analytic environments for cyber security with spatio-temporal marks are currently limited to traditional statistical methods like spatial-temporal outlier detection and hotspot detection [2]. 1 Furthermore, much of the current work in large scale AbstractIn this paper, we propose a framework for processing and analysing large-scale spatiotemporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information). In this work, we extend a spatial point pattern analysis method (the Morisita index) with meta-data analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis and demonstrate its practical use. The resulting framework is robust and has the capability to detect anomalies among large-scale spatio-temporal data using meta-data based on point pattern analysis. It returns visualized reports to end users.

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