# Shallow Learning Techniques for Early Detection and Classification of Cyberattacks over MQTT IoT Networks

## TL;DR

This paper introduces a lightweight cybersecurity system using shallow learning to detect and classify cyberattacks on MQTT-based IoT networks.

## Contribution

A novel lightweight multiclassifier based on shallow learning for detecting DoS and intrusion attacks in MQTT IoT networks.

## Key findings

- The system achieves over 99% accuracy in detecting cyberattacks on MQTT networks.
- It demonstrates an F1-score greater than 80% for intrusion attack classification.
- The approach is suitable for resource-constrained IoT devices.

## Abstract

What are the main findings?
This work proposes a lightweight multiclassifier for cyberattack detection on MQTT networks. The classifier, based on shallow learning techniques, enables the deployment in resorce-constrained devices, targeting Denial-of-Service and Intrusion attacks in IoT MQTT environments.Development of an intrusion detector system based on shallow learning techniques.

This work proposes a lightweight multiclassifier for cyberattack detection on MQTT networks. The classifier, based on shallow learning techniques, enables the deployment in resorce-constrained devices, targeting Denial-of-Service and Intrusion attacks in IoT MQTT environments.

Development of an intrusion detector system based on shallow learning techniques.

What is the implication of the main finding?
The detection and classification of cyberattacks on IoT networks over MQTT is enabled.

The detection and classification of cyberattacks on IoT networks over MQTT is enabled.

The increasing global connectivity, driven by the expansion of the Internet of Things (IoT), is generating a significant increase in system vulnerabilities. Cyberattackers exploit the computing and processing limitations of typical IoT devices and take advantage of inherent vulnerabilities in wireless networks and protocols to attack networks, compromise infrastructure, and cause damage. This paper presents a shallow learning multiclassifier approach for detecting and classifying cyberattacks on IoT networks. Specifically, it addresses MQTT networks, widely used in the IoT, to detect Denial-of-Service (DoS) and Intrusion attacks, using inter-device communication data as a basis. The use of shallow learning techniques allows this cybersecurity system to be implemented on resource-constrained devices, enabling local network monitoring and, consequently, increasing security and incident response capabilities by detecting and identifying attacks. The proposed system is validated on a real dataset obtained from an IoT system over MQTT, demonstrating its correct operation by achieving an accuracy greater than 99% and F1-score greater than 80% in the detection of Intrusion attacks.