Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning

arXiv:2404.05130v1 Announce Type: new
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 …

adoption arxiv attackers concept cs.cr cyber cyber threat cyber threat detection data date detection enforcement evasive federated federated learning gdpr good machine machine learning malware performance privacy protection regulations security threat threat data threat detection up-to-date