Seminar in Communication Networks:
Learning, Reasoning and Control Spring 2020

Feb 20 Session 1 Introduction to the Course

• Slides

• Knowledge-Defined Networking: Paper
• A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities: Paper

Mar 5 Session 2 Network Measurements: Traffic Analysis and Classification

• Neural Packet Classification: Paper

• Sibyl: A Practical Internet Route Oracle: Paper
• Realtime Classification for Encrypted Traffic: Paper
• Deep Learning for Encrypted Traffic Classification: An Overview: Paper

Mar 12 Session 3 Network Measurements: Anomaly Detection

• Detecting Credential Spearphishing Attacks in Enterprise Settings: Paper

• Outside the Closed World: On Using ML for Network Intrusion Detection: Paper
• Demystifying Deep Learning in Networking: Paper
• On the Effectiveness of Machine and Deep Learning for Cyber Security: Paper

Mar 19 Session 4 Network Adaptation: Traffic Optimization

• AuTO: Scaling Deep Reinforcement Learning for Datacenter-Scale Automatic Traffic Optimization: Paper

• Learning Scheduling Algorithms for Data Processing Clusters: Paper
• Why (and How) Networks Should Run Themselves: Paper
• Resource Management with Deep Reinforcement Learning: Paper

Mar 26 Session 5 Network Adaptation: Routing (Part 1)

• Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning: Paper

• CherryPick: Adaptively Unearthing the Best Cloud Configurations
  for BigData Analytics: Paper
• Learning to Route: Paper
• DeepConfig: Automating Data Center Network Topologies Management with Machine Learning: Paper
• Configtron: Tackling Network Diversity with Heterogeneous Configurations: Paper

Apr 2 Session 6 Network Adaptation: Routing (Part 2)

• Contra: A Programmable System for Performance-aware Routing: Paper

• (Self) Driving Under the Influence: Intoxicating Adversarial Network Inputs: Paper
• Performance-Driven Internet Path Selection: Paper

Apr 9 Session 7 Congestion Control: Learning Algorithms

• PCC Vivace: Online-Learning Congestion Control: Paper

• An Experimental Study of the Learnability of Congestion Control: Paper
• TCP ex Machina: Computer-Generated Congestion Control: Paper
• Congestion Control Throwdown: Paper

Apr 23 Session 8 Congestion Control: Using Programmable Networks

• HPCC: High Precision Congestion Control: Paper

Apr 30 Session 9 Application-level Adaptation (Part 1)

• CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction: Paper

• Neural Adaptive Video Streaming with Pensieve: Paper
• Inferring Netflix User Experience from Broadband Network Measurement: Paper

May 7 Session 10 Application-level Adaptation (Part 2)

• AWStream: Adaptive Wide-Area Streaming Analytics: Paper

• Pytheas: Enabling Data-Driven Quality of Experience Optimization Using Group-Based Exploration-Exploitation: Paper
• CFA: A Practical Prediction System for Video QoE Optimization: Paper

May 14 Session 11 In-Network Machine Learning

• Scaling Distributed Machine Learning with In-Network Aggregation: Paper

• Can the Network be the AI Accelerator?: Paper
• In-network Neural Networks: Paper
• pForest: In-Network Inference with Random Forests: Paper

May 28 Session 12 Machine-Learning Tools and Techniques

• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection: Paper

• Towards Oblivious Network Analysis using Generative Adversarial Networks: Paper
• Flow-based Network Traffic Generation using Generative Adversarial Networks: Paper
• Bringing a GAN to a Knife-fight: Adapting Malware Communication to Avoid Detection: Paper