Machine Learning and Data Networks

Primer paper that provides "an efficient start for anybody doing research regarding machine learning for networks or using networks for machine learning."

Summary article by a network giant

Summary of "fundamental techniques, specific frameworks, and access to relevant datasets"

"This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed."

"Explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems"

"The platform where the machine learning community collaborates on models, datasets, and applications."

"Join over 29M+ machine learners to share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project."

"Explore and extend models from the latest cutting edge research"

"Build enterprise-grade AI projects with secure and supported Canonical MLOps. Develop on your Ubuntu workstation using Charmed Kubeflow or Charmed MLFlow and scale up quickly with open source tooling in every part of your stack."

"Install a well-known model like DeepSeek R1 or Qwen 2.5 VL with a single command, and get the silicon-optimized AI engine automatically."

"An end-to-end demo that will walk you through setting up a scalable model training environment"

"This blog aims to provide an in-depth look at Ubuntu AI, covering fundamental concepts, usage methods, common practices, and best practices."

"Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters."

"OpenVINO is an open-source toolkit for deploying performant AI solutions in the cloud, on-prem, and on the edge alike. Develop your applications with both generative and conventional AI models, coming from the most popular model frameworks. Convert, optimize, and run inference utilizing the full potential of Intel hardware."

"A comprehensive dataset derived from 40 weeks of traffic transmitted by 275,000 active IP addresses in the CESNET3 network—an ISP network serving approximately half a million customers daily."

"A mathematical model for distributed denial-of-service attacks is proposed in this study. Machine learning algorithms such as Logistic Regression and Naive Bayes, are used to detect attacks and normal scenarios."

"This study proposes a machine-learning-based framework to enhance DDoS attack detection and mitigation, employing Random Forest, XGBoost, and Long Short-Term Memory (LSTM) models."

"A PCA-based Enhanced Distributed DDoS Attack Detection (EDAD) framework has been proposed. Various Machine Learning (ML) algorithms and feature selection techniques have been used to detect DDoS attacks. Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbours (KNN), Decision Tree (DT) supervised models, and Principle Component Analysis (PCA) feature selection method are used to differentiate between attack and regular traffic."

"Examines the application of twelve leading Machine Learning (ML) techniques, utilizing the Pycaret module, to effectively analyze Distributed Denial of Service (DDoS) attacks."