Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/114732
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) | URI: | https://hdl.handle.net/10316/114732 | 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 |
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