Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy

Yaoqi Yang, Yong Chen, Jiacheng Wang, Geng Sun, Dusit Niyato, Zhu Han

TL;DR

The paper addresses security challenges in the Low Altitude Economy (LAE) posed by malicious aircraft intrusions and argues that ensemble learning can enhance intrusion detection systems (IDS) in the LAE by leveraging diverse models to handle sparse, heterogeneous data in dynamic, resource-constrained environments. It surveys the secure LAE framework (detection, identification, localization, authentication) and presents case-study-backed evidence that ensemble methods improve robustness and accuracy. A practical ensemble-based malicious aircraft tracking framework combining YOLOX and Fast R-CNN is proposed, with a five-step workflow and numerical results showing improved precision-recall and detection performance. The work also discusses open issues such as real-time processing, integration with other alarm techniques, and trust in decision-making, outlining future directions for deploying ensemble-enabled secure LAE in real-world settings.

Abstract

Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth. However, this burgeoning field is vulnerable to security threats, particularly malicious aircraft intrusion attacks. To address the above concerns, intrusion detection systems (IDS) can be used to defend against malicious aircraft intrusions in LAE. Whereas, due to the heterogeneous data, dynamic environment, and resource-constrained devices within LAE, current IDS face challenges in detection accuracy, adaptability, and resource utilization ratio. In this regard, due to the inherent ability to combine the strengths of multiple models, ensemble learning can realize more robust and diverse anomaly detection further enhance IDS accuracy, thereby improving robustness and efficiency of the secure LAE. Unlike single-model approaches, ensemble learning can leverage the collective knowledge of its constituent models to effectively defend the malicious aircraft intrusion attacks. Specifically, this paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study. We first establish the rationale for ensemble learning and then review research areas and potential solutions, demonstrating the necessities and benefits of applying ensemble learning to secure LAE. Subsequently, we propose a framework of ensemble learning-enabled malicious aircrafts tracking in the secure LAE, where its feasibility and effectiveness are evaluated by the designed case study. Finally, we conclude by outlining promising future research directions for further advancing the ensemble learning-enabled secure LAE.

Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy

TL;DR

The paper addresses security challenges in the Low Altitude Economy (LAE) posed by malicious aircraft intrusions and argues that ensemble learning can enhance intrusion detection systems (IDS) in the LAE by leveraging diverse models to handle sparse, heterogeneous data in dynamic, resource-constrained environments. It surveys the secure LAE framework (detection, identification, localization, authentication) and presents case-study-backed evidence that ensemble methods improve robustness and accuracy. A practical ensemble-based malicious aircraft tracking framework combining YOLOX and Fast R-CNN is proposed, with a five-step workflow and numerical results showing improved precision-recall and detection performance. The work also discusses open issues such as real-time processing, integration with other alarm techniques, and trust in decision-making, outlining future directions for deploying ensemble-enabled secure LAE in real-world settings.

Abstract

Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth. However, this burgeoning field is vulnerable to security threats, particularly malicious aircraft intrusion attacks. To address the above concerns, intrusion detection systems (IDS) can be used to defend against malicious aircraft intrusions in LAE. Whereas, due to the heterogeneous data, dynamic environment, and resource-constrained devices within LAE, current IDS face challenges in detection accuracy, adaptability, and resource utilization ratio. In this regard, due to the inherent ability to combine the strengths of multiple models, ensemble learning can realize more robust and diverse anomaly detection further enhance IDS accuracy, thereby improving robustness and efficiency of the secure LAE. Unlike single-model approaches, ensemble learning can leverage the collective knowledge of its constituent models to effectively defend the malicious aircraft intrusion attacks. Specifically, this paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study. We first establish the rationale for ensemble learning and then review research areas and potential solutions, demonstrating the necessities and benefits of applying ensemble learning to secure LAE. Subsequently, we propose a framework of ensemble learning-enabled malicious aircrafts tracking in the secure LAE, where its feasibility and effectiveness are evaluated by the designed case study. Finally, we conclude by outlining promising future research directions for further advancing the ensemble learning-enabled secure LAE.
Paper Structure (20 sections, 3 figures, 1 table)

This paper contains 20 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Overview of Secure Low Altitude Economy and Ensemble Learning
  3. Secure Low Altitude Economy
  4. Ensemble Learning
  5. Ensemble Learning for Secure LAE
  6. Research Focuses and Solutions
  7. Ensemble Learning for Aircrafts Detection
  8. Ensemble Learning for Aircrafts Identification
  9. Ensemble Learning for Aircrafts Localization
  10. Ensemble Learning for Aircrafts Authentication
  11. Lessons learned
  12. Ensemble Learning-based Malicious Aircrafts Tracking in Low Altitude Economy
  13. Research Motivation
  14. Proposed Framework
  15. Case Study and Numerical Results
  16. ...and 5 more sections

Figures (3)

  • Figure 1: The system model of secure LAE, which mainly includes the detection, identification, localization, and authentication of malicious aircrafts.
  • Figure 2: The framework for the malicious aircraft tracking with ensemble learning in secure LAE. The ensemble model consists of the YOLOX and Fast R-CNN detectors. It can not only detect and identify the malicious aircrafts with confidence scores, but also localize the malicious aircrafts with the predicted bounding box information.
  • Figure 3: Performance evaluation of ensemble model and individual detectors. (a) Precision-recall curve of the individual and ensemble detectors. (b) Average precision and mean IoU performances of the individual and ensemble detectors.