Multivariate Statistical Network Monitoring–Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems

Roberto Magán-Carrión, José Camacho, Gabriel Maciá-Fernández and à ngel Ruíz-Zafra

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 5, 1550147720921309

Abstract: Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart health care systems are some examples of these applications. In this totally connected scenario, some security issues arise due to the large number of devices and communications. In this way, new solutions for monitoring and detecting security events are needed to address new challenges brought about by this scenario, among others, the real-time requirement allowing quick security event detection and, consequently, quick response to attacks. In this sense, Intrusion Detection Systems are widely used though their evaluation often relies on the use of predefined network datasets that limit their application in real environments. In this work, a real-time and ready-to-use tool for monitoring and detecting security events is introduced. The Multivariate Statistical Network Monitoring–Sensor is based on the Multivariate Statistical Network Monitoring methodology and provides an alternative way for evaluating Multivariate Statistical Network Monitoring–based Intrusion Detection System solutions. Experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for complex scenarios, such as those found in smart cities or Internet of Things ecosystems.

Keywords: Multivariate Statistical Network Monitoring; sensor; monitoring; anomaly detection; Intrusion Detection System; security; communication networks; Internet of Things; smart cities (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:5:p:1550147720921309

DOI: 10.1177/1550147720921309

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