Detection of Periodical Patterns and Contextual Anomalies in Data Streams

Abstract

We present SDOoop, a streaming data analysis algorithm that spots contextual anomalies undetectable by traditional methods, while enabling the inspection of data geometries, clusters and temporal patterns. We used SDOoop to model real network communications in critical infrastructures. We also evaluated SDOoop with data from intrusion detection and natural science domains and obtained performances equivalent or superior to state-of-the-art approaches. SDOoop is ideal for big data, being able to instantly process large volumes of information.