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

• URL: http://arxiv.org/abs/2501.13213v1
• Date: Wed, 22 Jan 2025 20:55:46 GMT
• Title: Distributed Intrusion Detection in Dynamic Networks of UAVs using Few-Shot Federated Learning
• Authors: Ozlem Ceviz, Sevil Sen, Pinar Sadioglu,
• Abstract summary: Intrusion detection in Flying Ad Hoc Networks (FANETs) is challenging due to communication costs, and privacy concerns. While Federated Learning (FL) holds promise for intrusion detection in FANETs, it also faces drawbacks such as large data requirements, power consumption, and time constraints. We propose Few-shot Federated Learning-based IDS (FSFL-IDS) to tackle intrusion detection challenges such as privacy, power constraints, communication costs, and lossy links.
• Score: 1.0923877073891446
• License:
• 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.

Related papers

• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• FedMADE: Robust Federated Learning for Intrusion Detection in IoT Networks Using a Dynamic Aggregation Method [7.842334649864372]
  Internet of Things (IoT) devices across multiple sectors has escalated serious network security concerns. Traditional Machine Learning (ML)-based Intrusion Detection Systems (IDSs) for cyber-attack classification require data transmission from IoT devices to a centralized server for traffic analysis, raising severe privacy concerns. We introduce FedMADE, a novel dynamic aggregation method, which clusters devices by their traffic patterns and aggregates local models based on their contributions towards overall performance.
  arXiv  Detail & Related papers  (2024-08-13T18:42:34Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security [1.2999518604217852]
  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.
  arXiv  Detail & Related papers  (2023-12-07T08:50:25Z)
• Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids [5.705281336771011]
  False data injection attack (FDIA) is one of the classes of attacks that target the smart measurement devices by injecting malicious data. We propose a federated learning-based scheme combined with a hybrid deep neural network architecture. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.
  arXiv  Detail & Related papers  (2023-06-17T20:29:55Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.