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BISITE Digital Innovation Hub, University of Salamanca, Edificio I+D+i, C/ Espejos s/n, 37007, Salamanca, Spain

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1 Papers

Distributed Machine Learning and Multi-Agent Systems for Enhanced Attack Detection and Resilience in IoT Networks

Topics: Intrusion Detection and Response; Security Frameworks, Architectures and Protocols; Security in IoT and Edge Computing; AI and Machine Learning for Security

Authors: Gustavo Funchal ¹ , Tiago Pedrosa ¹ , Fernando de la Prieta ² and Paulo Leitão ¹

Affiliations: ¹ Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal , ² BISITE Digital Innovation Hub, University of Salamanca, Edificio I+D+i, C/ Espejos s/n, 37007, Salamanca, Spain

Digital Object Identifier: 10.5220/0013154400003899

Proceedings of ICISSP 2025, ISBN: 978-989-758-735-1

Quick Abstract

The exponential growth of connected devices, including sensors, mobile devices, and various Internet of Things (IoT) devices, has resulted in a substantial increase in data generation. Traditionally, data analysis involves transferring data to cloud computing systems, leading to latency issues and excessive network traffic. Edge computing emerges as a promising solution by bringing processing closer to the data sources. However, edge computing faces challenges, particularly in terms of limited computational power, which can create constraints in the execution of machine learning (ML) tasks. This paper aims to analyze strategies for distributing ML tasks among multiple nodes based on multi-agent systems (MAS) technology to have a collaborative approach and compare these strategies to provide an overview of best practices for achieving the optimal performance in intrusion detection for Industrial Internet of Things (IIoT). In this way, the well-known CICIoT2023 data set was used, and centralized and distributed ML techniques were implemented, and evaluated. The distributed edge ML approach achieved promising results, presenting an improvement of between 7.73% and 32.18% in the correction of wrong predictions of detection of attacks on IoT devices, significantly improving the precision and recall of the applied techniques.