收集了部分将机器学习应用于网络流量分类的论文

heiFIP: A tool to convert network traffic into images for ML use cases

CSNet 23 - A generalizable approach for network flow image representation for deep learning

Code and example data for transcriptional-data-guided brain network classification

Your all in one QoS toolkit for distributed systems. Actively monitor, enforce, and test for peak performance. Guarantee low latency, high throughput, and constant availability with sentinel based metrics, policy management, automated testing, alerting, and rich reporting.

IP enrichment API — geolocation, ASN, cloud/VPN/Tor detection. curl-friendly, self-hostable, built in Rust.

CyberBERT is a deep learning model for network traffic classification, leveraging the DistilBERT architecture. It processes network flow data to classify various types of network traffic, including benign and malicious patterns, with high accuracy.

Opportunities and challenges in partitioning the graph measure space of real-world networks