A Hybrid Federated Learning Framework for Privacy-Preserving Near-Real-Time Intrusion Detection in IoT Environments DOI Open Access

Glauco Rampone,

Taras Ivaniv,

Salvatore Rampone

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1430 - 1430

Published: April 2, 2025

The proliferation of Internet Things (IoT) devices has introduced significant challenges in cybersecurity, particularly the realm intrusion detection. While effective, traditional centralized machine learning approaches often compromise data privacy and scalability due to need for aggregation. In this study, we propose a federated framework near-real-time detection IoT environments. Federated enables decentralized model training across multiple without exchanging raw data, thereby preserving reducing communication overhead. Our approach builds upon previously proposed hybrid model, which combines deployed on with second-level cloud-based analysis. This previous work required all be passed cloud aggregate form, limiting security. We extend incorporate learning, allowing distributed while maintaining high accuracy privacy. evaluate performance our federated-learning-based against focusing retention, efficiency, preservation. experiments utilize actual attack partitioned nodes. results demonstrate that not only offers advantages terms but also retains competitive accuracy. paper explores integration infrastructure, leveraging platforms such as Databricks Google Cloud Storage. discuss benefits implementing environment, including use Apache Spark MLlib scalable training. show algorithms used maintain an excellent identification (98% logistic R=regression, 97% SVM, 100% Random Forest). report very short time (less than 11 s single machine). low application is confirmed (0.16 over 1,697,851 packets). findings highlight potential viable solution enhancing cybersecurity ecosystems, paving way further research privacy-preserving techniques.

Language: Английский

A Hybrid Federated Learning Framework for Privacy-Preserving Near-Real-Time Intrusion Detection in IoT Environments DOI Open Access

Glauco Rampone,

Taras Ivaniv,

Salvatore Rampone

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1430 - 1430

Published: April 2, 2025

The proliferation of Internet Things (IoT) devices has introduced significant challenges in cybersecurity, particularly the realm intrusion detection. While effective, traditional centralized machine learning approaches often compromise data privacy and scalability due to need for aggregation. In this study, we propose a federated framework near-real-time detection IoT environments. Federated enables decentralized model training across multiple without exchanging raw data, thereby preserving reducing communication overhead. Our approach builds upon previously proposed hybrid model, which combines deployed on with second-level cloud-based analysis. This previous work required all be passed cloud aggregate form, limiting security. We extend incorporate learning, allowing distributed while maintaining high accuracy privacy. evaluate performance our federated-learning-based against focusing retention, efficiency, preservation. experiments utilize actual attack partitioned nodes. results demonstrate that not only offers advantages terms but also retains competitive accuracy. paper explores integration infrastructure, leveraging platforms such as Databricks Google Cloud Storage. discuss benefits implementing environment, including use Apache Spark MLlib scalable training. show algorithms used maintain an excellent identification (98% logistic R=regression, 97% SVM, 100% Random Forest). report very short time (less than 11 s single machine). low application is confirmed (0.16 over 1,697,851 packets). findings highlight potential viable solution enhancing cybersecurity ecosystems, paving way further research privacy-preserving techniques.

Language: Английский

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