Please use this identifier to cite or link to this item:
Title: An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices
Authors: Holubenko, Vitalina 
Silva, Paulo 
Bento, Carlos 
Keywords: Intrusion Detection System; Federated AI; Machine Learning; Internet of Things; Security; Privacy
Issue Date: 23-Jun-2023
Publisher: IEEE
Project: ARCADIANIoT - Autonomous Trust, Security and Privacy Management Framework for IoT, Grant Agreement Number: 101020259. H2020-SU-DS02-2020. 
Serial title, monograph or event: Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
Abstract: The current amount of IoT devices and their limitations has come to serve as a motivation for malicious entities to take advantage of such devices and use them for their own gain. To protect against cyberattacks in IoT devices, Machine Learning techniques can be applied to Intrusion Detection Systems. Moreover, privacy related issues associated with centralized approaches can be mitigated through Federated Learning. This work proposes a Host-based Intrusion Detection Systems that leverages Federated Learning and Multi-Layer Perceptron neural networks to detected cyberattacks on IoT devices with high accuracy and enhancing data privacy protection.
Description: Paper accepted in 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC)
DOI: 10.1109/CCNC51644.2023.10060443
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
FCTUC Eng.Informática - Artigos em Revistas Internacionais

Files in This Item:
Show full item record

Page view(s)

checked on May 22, 2024


checked on May 22, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.