PACKETCLIP: multi-modal embedding of network traffic and language for cybersecurity reasoning - Masukawa, Ryozo; Yun, Sanggeon; Jeong, Sungheon; Huang, Wenjun; Ni, Yang; Bryant, Ian; Bastian, Nathaniel D.; Imani, Mohsen (Frontiers in Artificial Intelligence, 2025)
Traffic classification is vital for cybersecurity, yet encrypted traffic poses significant challenges. We introduce PACKETCLIP which is a multi-modal framework combining packet data with natural language semantics through contrastive pre-training and hierarchical Graph Neural Network (GNN) reasoning. PACKETCLIP integrates semantic reasoning with efficient classification, enabling robust detection of anomalies in encrypted network flows. By aligning textual descriptions with packet behaviors, PACKETCLIP offers enhanced interpretability, scalability, and practical applicability across diverse security scenarios. With a 95% mean AUC, an 11.6% improvement over baselines, and a 92% reduction in intrusion detection training parameters, it is ideally suited for real-time anomaly detection. By bridging advanced machine-learning techniques and practical cybersecurity needs, PACKETCLIP provides a foundation for scalable, efficient, and interpretable solutions to tackle encrypted traffic classification and network intrusion detection challenges in resource-constrained environments.