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

2026-01-19

AI Summary

This work proposes a novel intrusion detection framework for Internet of Federated Things (IoFT) networks that integrates self-supervised representation learning with differential privacy to address challenges posed by data imbalance, privacy sensitivity, and the difficulty of detecting rare attacks. The approach employs masked feature reconstruction during pretraining to enhance the model’s ability to represent infrequent attack patterns, while incorporating differential privacy mechanisms during training to safeguard sensitive information. To the best of our knowledge, this is the first study to combine self-supervised learning with differential privacy specifically for IoFT intrusion detection. Furthermore, SHAP analysis is leveraged to investigate how the privacy-preserving mechanism influences feature importance. Evaluated on the ECU-IoFT dataset, the proposed model achieves 98% accuracy and a 99% F1-score, demonstrating significant improvements in rare attack detection while maintaining strong privacy guarantees.

Technology Category

• Machine Learning: Privacy
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• IoFT
• rare attack detection
• data imbalance
• privacy preservation
• intrusion detection

Innovation

• self-supervised learning
• differential privacy
• rare attack detection
• IoFT security
• masked feature reconstruction