Network Traffic

Network traffic analysis focuses on understanding and interpreting the flow of data across networks, aiming to improve network security, performance, and management. Current research emphasizes the application of advanced machine learning models, including deep learning architectures like LSTMs, CNNs, Transformers, and Graph Neural Networks, along with techniques like transfer learning and data augmentation to address challenges like limited data and encrypted traffic. These advancements are crucial for enhancing cybersecurity (e.g., detecting botnets and ransomware), optimizing network resource allocation, and improving the efficiency of network monitoring and management in diverse settings, from small ISPs to large-scale urban networks.

Papers

October 26, 2022

Active Learning Framework to Automate NetworkTraffic Classification
Jaroslav Pešek, Dominik Soukup, Tomáš Čejka
Network Traffic Traffic Classification Active Learning Framework Active Learning Algorithm Machine Learning Workflow

September 16, 2022

Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning
Tohid Behdadnia, Geert Deconinck
Anomaly Detection Power Grid Network Traffic Cyber Physical Deep Transfer Learning Multi Scale Traffic

September 13, 2022

Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics
C. Chen, Y. P. Huang, W. H. K. Lam, T. L. Pan, S. C. Hsu, A. Sumalee, R. X. Zhong
Raw Data Efficient Reinforcement Learning Network Traffic Traffic Dynamic Integral Reinforcement Learning Perimeter Defense Problem

July 12, 2022

A new hope for network model generalization
Alexander Dietmüller, Siddhant Ray, Romain Jacob, Laurent Vanbever
Strong Generalization Network Traffic Generalizable Machine Learning Graph Generalization

May 8, 2022

Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature
Xueyuan Duan, Yu Fu, Kun Wang
Anomaly Detection Network Traffic Traffic Classification Multi Scale Residual Network Traffic Anomaly Detection

May 3, 2022

An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model
Sajal Saha, Anwar Haque, Greg Sidebottom
Empirical Study Traffic Prediction Network Traffic Prediction Model Integrated Moving Average Rolling Diffusion

April 16, 2022

A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis
Lingzi Kong, Daoqi Han, Junmei Ding, Mingrui Fan, Yueming Lu
Smart Grid Network Traffic Hierarchical Object Detection

March 21, 2022

December 3, 2021

Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System
Nasreen Fathima, Akshara Pramod, Yash Srivastava, Anusha Maria Thomas, Syed Ibrahim S P, Chandran K R
Deep Learning Intrusion Detection System Network Intrusion Detection Network Traffic Shallow Learning Sparse Event

Network Traffic

Papers

Active Learning Framework to Automate NetworkTraffic Classification

Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning

Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics

A new hope for network model generalization

Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature

An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model

A Hierarchical Terminal Recognition Approach based on Network Traffic Analysis

FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic

A Policy Driven AI-Assisted PoW Framework

Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System