The Internet of Flying Things (IoFT) plays a vital role in modern applications such as aerial surveillance and smart mobility. However, it remains highly vulnerable to cyberattacks that threaten the confidentiality, integrity, and availability of sensitive data. Developing effective intrusion detection systems (IDS) for IoFT networks faces key challenges, including data imbalance, privacy concerns, and the limited capability of traditional models to detect rare but potentially damaging cyber threats. In this work, we propose PrivFly, a privacy-preserving IDS framework that integrates self-supervised representation learning and differential privacy (DP) to enhance detection performance in imbalanced IoFT network traffic. We propose a masked feature reconstruction module for self-supervised pretraining, improving feature representations and boosting rare-class detection. Differential privacy is applied during training to protect sensitive information without significantly compromising model performance. In addition, we conduct a SHapley additive explanations (SHAP)-based analysis to evaluate the impact of DP on feature importance and model behavior. Experimental results on the ECU-IoFT dataset show that PrivFly achieves up to 98% accuracy and 99% F1-score, effectively balancing privacy and detection performance for secure IoFT systems.

翻译：飞行物联网（IoFT）在航空监视与智能移动等现代应用中发挥着至关重要的作用。然而，其极易受到网络攻击的威胁，危及敏感数据的机密性、完整性与可用性。为IoFT网络开发有效的入侵检测系统（IDS）面临若干关键挑战，包括数据不平衡、隐私顾虑，以及传统模型检测罕见但潜在危害巨大的网络威胁的能力有限。本文提出PrivFly，一种隐私保护的IDS框架，它集成了自监督表示学习与差分隐私（DP），以提升在不平衡IoFT网络流量中的检测性能。我们设计了一个掩码特征重构模块用于自监督预训练，以改进特征表示并增强罕见类别的检测。在训练过程中应用差分隐私以保护敏感信息，同时不会显著影响模型性能。此外，我们进行了基于SHapley加性解释（SHAP）的分析，以评估DP对特征重要性和模型行为的影响。在ECU-IoFT数据集上的实验结果表明，PrivFly实现了高达98%的准确率和99%的F1分数，有效地平衡了安全IoFT系统中的隐私保护与检测性能。