{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/enabling-privacy-preserving-cyber-threat","title":"Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning","arxiv_id":"2404.05130","date":"2024-04-08","proceeding":null,"authors":["Yu Bi","Yekai Li","Xuan Feng","Xianghang Mi"],"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.","url_abs":"https://arxiv.org/abs/2404.05130v1","url_pdf":"https://arxiv.org/pdf/2404.05130v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"enabling-privacy-preserving-cyber-threat","repo_url":"https://github.com/chasesecurity/fostering_cyber_threat_detection_through_fl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}