Generic Multi-modal Representation Learning for Network Traffic Analysis

arXiv:2405.02649v1 Announce Type: new
Abstract: Network traffic analysis is fundamental for network management, troubleshooting, and security. Tasks such as traffic classification, anomaly detection, and novelty discovery are fundamental for extracting operational information from network data and measurements. We witness the shift from deep packet inspection and basic machine learning to Deep Learning (DL) approaches where researchers define and test a custom DL architecture designed for each specific problem. We here advocate the need for a general DL architecture flexible enough …

abstract analysis anomaly anomaly detection arxiv basic classification cs.ai cs.lg data deep learning detection discovery fundamental information machine machine learning management modal multi-modal network network management representation representation learning security shift tasks traffic traffic analysis troubleshooting type witness