# MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and Systems

## TL;DR

This paper introduces MSNM-Sensor, a practical network monitoring tool based on multivariate statistical methods, designed to detect security anomalies in complex, large-scale IoT and smart city environments.

## Contribution

It presents a ready-to-use anomaly detection tool utilizing MSNM methodology tailored for smart city and IoT security monitoring.

## Key findings

- Proven effectiveness in detecting known security attacks
- Suitable for complex smart city and IoT scenarios
- Early development stage with promising experimental results

## Abstract

Technology evolves quickly. Low-cost and ready-to-connect devices are designed to provide new services and applications. Smart grids or smart healthcare systems are some examples of these applications, all of which are in the context of smart cities. In this total-connectivity scenario, some security issues arise since the larger the number of connected devices is, the greater the surface attack dimension. 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 large number of devices to monitor, the large amount of data to manage and the real-time requirement to provide quick security event detection and, consequently, quick response to attacks. In this work, a practical and ready-to-use tool for monitoring and detecting security events in these environments is developed and introduced. The tool is based on the Multivariate Statistical Network Monitoring (MSNM) methodology for monitoring and anomaly detection and we call it MSNM-Sensor. Although it is in its early development stages, experimental results based on the detection of well-known attacks in hierarchical network systems prove the suitability of this tool for more complex scenarios, such as those found in smart cities or IoT ecosystems.