Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning

• URL: http://arxiv.org/abs/2404.05130v1
• Date: Mon, 8 Apr 2024 01:16:56 GMT
• Title: Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning
• Authors: Yu Bi, Yekai Li, Xuan Feng, Xianghang Mi,
• Abstract summary: This study systematically profiles the (in)feasibility of learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning.
• Score: 4.475514208635884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite achieving good performance and wide adoption, machine learning based security detection models (e.g., malware classifiers) are subject to concept drift and evasive evolution of attackers, which renders up-to-date threat data as a necessity. However, due to enforcement of various privacy protection regulations (e.g., GDPR), it is becoming increasingly challenging or even prohibitive for security vendors to collect individual-relevant and privacy-sensitive threat datasets, e.g., SMS spam/non-spam messages from mobile devices. To address such obstacles, this study systematically profiles the (in)feasibility of federated learning for privacy-preserving cyber threat detection in terms of effectiveness, byzantine resilience, and efficiency. This is made possible by the build-up of multiple threat datasets and threat detection models, and more importantly, the design of realistic and security-specific experiments. We evaluate FL on two representative threat detection tasks, namely SMS spam detection and Android malware detection. It shows that FL-trained detection models can achieve a performance that is comparable to centrally trained counterparts. Also, most non-IID data distributions have either minor or negligible impact on the model performance, while a label-based non-IID distribution of a high extent can incur non-negligible fluctuation and delay in FL training. Then, under a realistic threat model, FL turns out to be adversary-resistant to attacks of both data poisoning and model poisoning. Particularly, the attacking impact of a practical data poisoning attack is no more than 0.14\% loss in model accuracy. Regarding FL efficiency, a bootstrapping strategy turns out to be effective to mitigate the training delay as observed in label-based non-IID scenarios.

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