Multivariate Statistical Network Monitoring–Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems
Metadata
Show full item recordEditorial
SAGE Publications
Materia
Multivariate Statistical Network Monitoring Sensor Monitoring Anomaly detection Intrusion Detection System Security Communication networks Internet of things Smart cities
Date
2020-04Referencia bibliográfica
Magán-Carrión, R., Camacho, J., Maciá-Fernández, G., & Ruíz-Zafra, Á. (2020). Multivariate Statistical Network Monitoring–Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems. International Journal of Distributed Sensor Networks, 16(5), [DOI: 10.1177/1550147720921309]
Sponsorship
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by Spanish MINECO (Ministerio de Economı´a y Competitividad) through projects TIN2014-60346-R, TIN2017-83494-R, and FEDER funds.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.