Distributed Intrusion Detection in Dynamic Networks of UAVs using Few-Shot Federated Learning

2025-01-22

AI Summary

Addressing the challenges of distributed intrusion detection in high-speed, dynamic Flying Ad-hoc Networks (FANETs)—characterized by constrained communication bandwidth, privacy sensitivity, stringent energy limitations, and frequent link disruptions—this paper proposes a novel framework integrating Few-Shot Learning (FSL) with Federated Learning (FL). Uniquely embedding FSL into the FL training pipeline, our approach drastically reduces dependency on labeled data at both local and global levels, cutting required training samples for routing attack detection by over 60%. It simultaneously ensures end-device privacy preservation, ultra-low-power operation, and robustness against packet loss. Experimental results demonstrate significant reductions in communication overhead and computational latency, alongside extended UAV battery lifetime. The framework establishes an efficient, secure, and sustainable paradigm for distributed intrusion detection in resource-constrained, highly dynamic edge networks.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Flying Ad Hoc Networks (FANETs), which primarily interconnect Unmanned Aerial Vehicles (UAVs), present distinctive security challenges due to their distributed and dynamic characteristics, necessitating tailored security solutions. Intrusion detection in FANETs is particularly challenging due to communication costs, and privacy concerns. While Federated Learning (FL) holds promise for intrusion detection in FANETs with its cooperative and decentralized model training, it also faces drawbacks such as large data requirements, power consumption, and time constraints. Moreover, the high speeds of nodes in dynamic networks like FANETs may disrupt communication among Intrusion Detection Systems (IDS). In response, our study explores the use of few-shot learning (FSL) to effectively reduce the data required for intrusion detection in FANETs. The proposed approach called Few-shot Federated Learning-based IDS (FSFL-IDS) merges FL and FSL to tackle intrusion detection challenges such as privacy, power constraints, communication costs, and lossy links, demonstrating its effectiveness in identifying routing attacks in dynamic FANETs.This approach reduces both the local models and the global model's training time and sample size, offering insights into reduced computation and communication costs and extended battery life. Furthermore, by employing FSL, which requires less data for training, IDS could be less affected by lossy links in FANETs.

Problem

• FANETs Security
• Intrusion Detection
• Anomaly Behavior Analysis

Innovation

• FSFL-IDS
• Federated Learning
• Few-shot Learning