PrivFly: A Privacy-Preserving Self-Supervised Framework for Rare Attack Detection in IoFT

TL;DR

PrivFly is a novel privacy-preserving self-supervised framework that enhances rare attack detection in IoFT networks by combining self-supervised learning and differential privacy, achieving high accuracy and F1-score.

Contribution

It introduces a self-supervised masked feature reconstruction module and applies differential privacy during training for IoFT intrusion detection.

Findings

Achieves up to 98% accuracy in detection

Attains 99% F1-score on ECU-IoFT dataset

Balances privacy preservation with detection performance

Abstract

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.