English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Unmanned aerial vehicles (UAVs) operating within Flying Ad-hoc Networks(FANETs) encounter security challenges due to the dynamic and distributednature of these networks. Previous studies predominantly focused on centralizedintrusion detection, assuming a central entity responsible for storing andanalyzing data from all devices.However, these approaches face challengesincluding computation and storage costs, along with a single point of failurerisk, threatening data privacy and availability. The widespread dispersion ofdata across interconnected devices underscores the necessity for decentralizedapproaches. This paper introduces the Federated Learning-based IntrusionDetection System (FL-IDS), addressing challenges encountered by centralizedsystems in FANETs. FL-IDS reduces computation and storage costs for bothclients and the central server, crucial for resource-constrained UAVs.Operating in a decentralized manner, FL-IDS enables UAVs to collaborativelytrain a global intrusion detection model without sharing raw data, thusavoiding the delay in decisions based on collected data, as is often the casewith traditional methods. Experimental results demonstrate FL-IDS's competitiveperformance with Central IDS (C-IDS) while mitigating privacy concerns, withthe Bias Towards Specific Clients (BTSC) method further enhancing FL-IDSperformance even at lower attacker ratios. Comparative analysis withtraditional intrusion detection methods, including Local IDS (L-IDS), shedslight on FL-IDS's strengths. This study significantly contributes to UAVsecurity by introducing a privacy-aware, decentralized intrusion detectionapproach tailored to UAV networks. Moreover, by introducing a realistic datasetfor FANETs and federated learning, our approach differs from others lackinghigh dynamism and 3D node movements or accurate federated data federations.

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