RT journal article
T1 Multivariate Statistical Network Monitoring-Sensor: An effective tool for real-time monitoring and anomaly detection in complex networks and systems
A1 Magán Carrión, Roberto
A1 Camacho, José
A1 Maciá Fernández, Gabriel
A1 Ruiz Zafra, Ángel
A2 Ingeniería Informática
K1 Multivariate Statistical Network Monitoring
K1 monitoring
K1 anomaly detection
K1 Intrusion Detection System
K1 security
K1 communication networks
K1 Internet of Things
K1 smart cities
AB 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
PB SAGE PUBLICATIONS INC
SN 1550-1477
YR 2020
FD 2020-05
LK http://hdl.handle.net/10498/23263
UL http://hdl.handle.net/10498/23263
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026