Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1785081216286851072 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10384678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT decaldasfilhofranciscolopes botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning AT soaressamuelcarlosmeneses botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning AT oroskielder botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning AT deoliveiraalbuquerquerobson botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning AT damatarafaelzerbinialves botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning AT demendoncafabioluciolopes botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning AT desousajuniorrafaeltimoteo botnetdetectionandmitigationmodelforiotnetworksusingfederatedlearning |