A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security

• URL: http://arxiv.org/abs/2312.04135v2
• Date: Fri, 15 Mar 2024 20:41:03 GMT
• Title: A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security
• Authors: Ozlem Ceviz, Pinar Sadioglu, Sevil Sen, Vassilios G. Vassilakis,
• Abstract summary: Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks. Previous studies predominantly focused on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices. This paper introduces the Federated Learning-based Intrusion Detection System (FL-IDS), addressing challenges encountered by centralized systems in FANETs.
• Score: 1.2999518604217852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs) encounter security challenges due to the dynamic and distributed nature of these networks. Previous studies predominantly focused on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices.However, these approaches face challenges including computation and storage costs, along with a single point of failure risk, threatening data privacy and availability. The widespread dispersion of data across interconnected devices underscores the necessity for decentralized approaches. This paper introduces the Federated Learning-based Intrusion Detection System (FL-IDS), addressing challenges encountered by centralized systems in FANETs. FL-IDS reduces computation and storage costs for both clients and the central server, crucial for resource-constrained UAVs. Operating in a decentralized manner, FL-IDS enables UAVs to collaboratively train a global intrusion detection model without sharing raw data, thus avoiding the delay in decisions based on collected data, as is often the case with traditional methods. Experimental results demonstrate FL-IDS's competitive performance with Central IDS (C-IDS) while mitigating privacy concerns, with the Bias Towards Specific Clients (BTSC) method further enhancing FL-IDS performance even at lower attacker ratios. Comparative analysis with traditional intrusion detection methods, including Local IDS (L-IDS), sheds light on FL-IDS's strengths. This study significantly contributes to UAV security by introducing a privacy-aware, decentralized intrusion detection approach tailored to UAV networks. Moreover, by introducing a realistic dataset for FANETs and federated learning, our approach differs from others lacking high dynamism and 3D node movements or accurate federated data federations.

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