An Efficient Privacy-preserving Intrusion Detection Scheme for UAV Swarm Networks
- URL: http://arxiv.org/abs/2511.22791v1
- Date: Thu, 27 Nov 2025 22:37:06 GMT
- Title: An Efficient Privacy-preserving Intrusion Detection Scheme for UAV Swarm Networks
- Authors: Kanchon Gharami, Shafika Showkat Moni,
- Abstract summary: Intrusion Detection System plays vital role in identifying potential security attacks to ensure the secure operation of UAV swarm networks.<n>This research aims to address these challenges by developing a novel lightweight and federated continuous learning-based IDS scheme.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid proliferation of unmanned aerial vehicles (UAVs) and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits of swarm-based UAV networks are growing significantly, they are vulnerable to various security attacks that can jeopardize the overall mission success by degrading their performance, disrupting decision-making, and compromising the trajectory planning process. The Intrusion Detection System (IDS) plays a vital role in identifying potential security attacks to ensure the secure operation of UAV swarm networks. However, conventional IDS primarily focuses on binary classification with resource-intensive neural networks and faces challenges, including latency, privacy breaches, increased performance overhead, and model drift. This research aims to address these challenges by developing a novel lightweight and federated continuous learning-based IDS scheme. Our proposed model facilitates decentralized training across diverse UAV swarms to ensure data heterogeneity and privacy. The performance evaluation of our model demonstrates significant improvements, with classification accuracies of 99.45% on UKM-IDS, 99.99% on UAV-IDS, 96.85% on TLM-UAV dataset, and 98.05% on Cyber-Physical datasets.
Related papers
- Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy [64.39232788946173]
Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth.<n>This paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study.
arXiv Detail & Related papers (2026-02-07T23:15:58Z) - Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
arXiv Detail & Related papers (2025-10-28T10:05:53Z) - When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks [68.45202147860537]
Low-altitude wireless networks (LAWNs) provide enhanced coverage, reliability, and throughput for diverse applications.<n>These networks face significant security vulnerabilities from both known and potential unknown eavesdroppers.<n>We propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array.
arXiv Detail & Related papers (2025-10-25T02:02:14Z) - LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks [57.27815890269697]
This work focuses on maximizing the secrecy rate in heterogeneous UAV networks (HetUAVNs) under energy constraints.<n>We introduce a Large Language Model (LLM)-guided multi-agent learning approach.<n>Results show that our method outperforms existing baselines in secrecy and energy efficiency.
arXiv Detail & Related papers (2025-07-23T04:22:57Z) - A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks [6.229535970620059]
We present an unsupervised autoencoder method trained entirely on benign network data for unseen attack detection in IoV networks.<n>We show that our method performs robustly for all unseen attack types, with roughly 99% accuracy on benign data and between 97% and 100% performance on anomaly data.
arXiv Detail & Related papers (2025-05-27T19:40:57Z) - Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation [0.0]
This paper proposes an innovative approach that leverages Knowledge Distillation to enhance the practicality of secure UAV navigation.
By integrating RL and FHE, our framework addresses vulnerabilities to adversarial attacks while enabling real-time processing of encrypted UAV camera feeds.
To mitigate FHE's latency, Knowledge Distillation is employed to compress the network, resulting in an impressive 18x speedup without compromising performance.
arXiv Detail & Related papers (2024-11-01T07:04:24Z) - Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks [1.749935196721634]
This paper presents a new intrusion detection system (IDS) for UAV networks.
A binary-tuple representation was used for encoding class labels, along with a deep learning-based approach employed for classification.
The proposed system enhances the intrusion detection by capturing complex class relationships and temporal network patterns.
arXiv Detail & Related papers (2024-06-14T21:29:15Z) - Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading [3.427366431933441]
Our framework incorporates exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and jamming threats.
We introduce a transformer-enhanced Deep Reinforcement Learning (DRL) approach to optimize task offloading processes.
arXiv Detail & Related papers (2024-04-06T17:41:00Z) - 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.<n>Previous studies focused predominantly on centralized intrusion detection, assuming a central entity responsible for storing and analyzing data from all devices.<n>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) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - Adversarial defense for automatic speaker verification by cascaded
self-supervised learning models [101.42920161993455]
More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems.
We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations.
Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
arXiv Detail & Related papers (2021-02-14T01:56:43Z)
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.