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Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning

The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This s...

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Autores principales: de Caldas Filho, Francisco Lopes, Soares, Samuel Carlos Meneses, Oroski, Elder, de Oliveira Albuquerque, Robson, da Mata, Rafael Zerbini Alves, de Mendonça, Fábio Lúcio Lopes, de Sousa Júnior, Rafael Timóteo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384678/
https://www.ncbi.nlm.nih.gov/pubmed/37514600
http://dx.doi.org/10.3390/s23146305
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author de Caldas Filho, Francisco Lopes
Soares, Samuel Carlos Meneses
Oroski, Elder
de Oliveira Albuquerque, Robson
da Mata, Rafael Zerbini Alves
de Mendonça, Fábio Lúcio Lopes
de Sousa Júnior, Rafael Timóteo
author_facet de Caldas Filho, Francisco Lopes
Soares, Samuel Carlos Meneses
Oroski, Elder
de Oliveira Albuquerque, Robson
da Mata, Rafael Zerbini Alves
de Mendonça, Fábio Lúcio Lopes
de Sousa Júnior, Rafael Timóteo
author_sort de Caldas Filho, Francisco Lopes
collection PubMed
description The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system.
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spelling pubmed-103846782023-07-30 Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning de Caldas Filho, Francisco Lopes Soares, Samuel Carlos Meneses Oroski, Elder de Oliveira Albuquerque, Robson da Mata, Rafael Zerbini Alves de Mendonça, Fábio Lúcio Lopes de Sousa Júnior, Rafael Timóteo Sensors (Basel) Article The Internet of Things (IoT) introduces significant security vulnerabilities, raising concerns about cyber-attacks. Attackers exploit these vulnerabilities to launch distributed denial-of-service (DDoS) attacks, compromising availability and causing financial damage to digital infrastructure. This study focuses on mitigating DDoS attacks in corporate local networks by developing a model that operates closer to the attack source. The model utilizes Host Intrusion Detection Systems (HIDS) to identify anomalous behaviors in IoT devices and employs network-based intrusion detection approaches through a Network Intrusion Detection System (NIDS) for comprehensive attack identification. Additionally, a Host Intrusion Detection and Prevention System (HIDPS) is implemented in a fog computing infrastructure for real-time and precise attack detection. The proposed model integrates NIDS with federated learning, allowing devices to locally analyze their data and contribute to the detection of anomalous traffic. The distributed architecture enhances security by preventing volumetric attack traffic from reaching internet service providers and destination servers. This research contributes to the advancement of cybersecurity in local network environments and strengthens the protection of IoT networks against malicious traffic. This work highlights the efficiency of using a federated training and detection procedure through deep learning to minimize the impact of a single point of failure (SPOF) and reduce the workload of each device, thus achieving accuracy of 89.753% during detection and increasing privacy issues in a decentralized IoT infrastructure with a near-real-time detection and mitigation system. MDPI 2023-07-11 /pmc/articles/PMC10384678/ /pubmed/37514600 http://dx.doi.org/10.3390/s23146305 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
de Caldas Filho, Francisco Lopes
Soares, Samuel Carlos Meneses
Oroski, Elder
de Oliveira Albuquerque, Robson
da Mata, Rafael Zerbini Alves
de Mendonça, Fábio Lúcio Lopes
de Sousa Júnior, Rafael Timóteo
Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
title Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
title_full Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
title_fullStr Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
title_full_unstemmed Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
title_short Botnet Detection and Mitigation Model for IoT Networks Using Federated Learning
title_sort botnet detection and mitigation model for iot networks using federated learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384678/
https://www.ncbi.nlm.nih.gov/pubmed/37514600
http://dx.doi.org/10.3390/s23146305
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