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.

翻译：低空经济（LAE）在提升社会福祉和推动经济增长方面具有巨大潜力。然而，这一新兴领域易受安全威胁，特别是恶意飞行器入侵攻击。为解决上述问题，可采用入侵检测系统（IDS）来防御LAE中的恶意飞行器入侵。然而，由于LAE中存在异构数据、动态环境及资源受限设备，现有IDS在检测精度、适应性和资源利用率方面面临挑战。鉴于此，集成学习因其固有的整合多模型优势的能力，可实现更鲁棒和多样化的异常检测，从而进一步提升IDS精度，增强安全LAE的鲁棒性和效率。与单模型方法不同，集成学习能够利用其组成模型的集体知识，有效防御恶意飞行器入侵攻击。具体而言，本文研究了面向安全LAE的集成学习，涵盖研究重点、解决方案及案例研究。我们首先阐述了集成学习的理论基础，随后综述了相关研究领域和潜在解决方案，论证了将集成学习应用于安全LAE的必要性与优势。接着，我们提出了一个支持集成学习的恶意飞行器追踪框架，用于安全LAE，并通过设计的案例研究评估了其可行性与有效性。最后，我们总结了未来有前景的研究方向，以进一步推动基于集成学习的安全LAE发展。