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A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security (2312.04135v2)

Published 7 Dec 2023 in cs.CR and cs.LG

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.

PDF HTML Abstract

Federated Learning-Based Intrusion Detection for UAV Privacy and Security

The paper under review discusses an innovative approach to Intrusion Detection Systems (IDS) specifically designed for Unmanned Aerial Vehicles (UAVs) operating within Flying Ad-hoc Networks (FANETs). UAVs have become integral to various critical applications such as surveillance and disaster management. However, the dynamic and distributed nature of FANETs poses significant security challenges, particularly when using traditional centralized intrusion detection approaches. Significant computational and storage burdens, alongside potential privacy violations and a single point of failure, necessitate exploring decentralized methodologies. This is where the Federated Learning-based IDS (FL-IDS) proposed in the paper becomes relevant, offering a decentralized and privacy-conscious solution to these challenges.

Methodology

FL-IDS operates on the principle of federated learning (FL), a decentralized machine learning approach that allows for privacy-preserving training by enabling UAVs to collaboratively build a global intrusion detection model without sharing raw data. This model addresses the unique challenges of FANETs by reducing computation and storage load on resource-constrained UAVs and eliminating the possible latency caused by data transmission to centralized servers. By aggregating locally updated model weights rather than raw data, FL-IDS maintains data privacy and effectively mitigates risks associated with unauthorized data access.

Experimental Results and Analysis

The paper utilizes realistic FANET scenarios to simulate sinkhole, blackhole, and flooding attacks against the Ad-Hoc On-Demand Distance Vector (AODV) routing protocol using the NS-3 simulator. These attacks were chosen due to their pertinence in exposing vulnerabilities in existing systems. The results demonstrate that FL-IDS achieves competitive performance relative to the traditional centralized IDS (C-IDS), particularly at higher attacker ratios, which signals a robust tendency to infer the anomaly patterns collaboratively even in challenging scenarios.

The quantitative results suggest that at higher attacker ratios, FL-IDS approaches the efficacy levels of C-IDS, particularly when employing methods such as Bias Towards Specific Clients (BTSC) to prioritize client contributions that enhance model accuracy. This strategy further bolsters FL-IDS's standing, marking a significant advancement over Local IDS (L-IDS), which demonstrated lower accuracy due to the lack of collaboration among individual nodes.

Implications and Future Directions

The implications of adopting FL-IDS extend beyond enhanced UAV security; they promote the integration of AI-driven security solutions into broader IoT and autonomous systems, where data privacy and distributed processing resources present similar challenges. The research opens avenues for employing additional federated learning methodologies, such as differential privacy or secure multi-party computations, to further enhance the security protocols in deployment environments that prioritize confidentiality alongside performance efficiency.

Looking forward, future iterations of FL-IDS can benefit from reducing communication costs further and optimizing the balance between model complexity and computation resource constraints. Exploring different federated learning algorithms and assessing their impact on broader network topographies would also be beneficial. Moreover, the creation of even more comprehensive datasets reflecting diverse attack vectors in variable node density environments could greatly fortify the empirical base from which FL-IDS solutions are derived.

In conclusion, the FL-IDS presented constitutes an important stride towards addressing the significant privacy and security challenges intrinsic to UAV operations in FANETs. Its ability to harness and improve upon decentralized data for intrusion detection while maintaining robustness in the face of increasing attack ratios underpins its applicability as a viable, scalable security framework in evolving network paradigms.

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Authors (4)
  1. Ozlem Ceviz (3 papers)
  2. Pinar Sadioglu (3 papers)
  3. Sevil Sen (8 papers)
  4. Vassilios G. Vassilakis (4 papers)
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