Deep Learning models for network traffic classification

The repository of ET-BERT, a network traffic classification model on encrypted traffic. The work has been accepted as The Web Conference (WWW) 2022 accepted paper.

Poseidon is a python-based application that leverages software defined networks (SDN) to acquire and then feed network traffic to a number of machine learning techniques. The machine learning algorithms classify and predict the type of device.

Network traffic classification library that requires minimal application payload

收集了部分将机器学习应用于网络流量分类的论文

using deep learning to classify the encrypted network traffic

Efficient Network Traffic Classification via Pre-training Unidirectional Mamba

[WWW'23] TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

Machine learning plugins for network traffic

Code for “FS-Net: A Flow Sequence Network For Encrypted Traffic Classification”

T. Shapira and Y. Shavitt, "FlowPic: A Generic Representation for Encrypted Traffic Classification and Applications Identification," in IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2021.3071441.

Analysis of the ISCX VPN-nonVPN Dataset 2016 for Encrypted Network Traffic Classification

AI & Machine Learning: Detection and Classification of Network Traffic Anomalies based on IoT23 Dataset

《基于卷积神经网络（CNN）的网络流量分类》优秀本科毕设相关文档

To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

Encrypted Network Traffic Classification using Deep Learning

The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.

FS-Net: A Flow Sequence Network For Encrypted Traffic Classification

Implementation of a multi-task model for encrypted network traffic classification based on transformer and 1D-CNN.

The use of machine learning to classify network traffic

No description available

Self-attentive deep learning method for online traffic classification and its interpretability (CN21 & NetAI20)

论文Encrypted Traffic Classification with One-dimensional Convolution Neural Networks的torch实现

code repository for the the paper "Encrypted Network Traffic Classification with Higher Order Graph Neural Network"

Source code for the paper: Adaptive Clustering-based Malicious Traffic Classification at the Network Edge (https://homepages.inf.ed.ac.uk/ppatras/pub/infocom21.pdf)

Wang Wei's End-to-end encrypted traffic classification with one-dimensional convolution neural networks (scripts and article))

Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.

基于IDS 2018数据集使用LightGBM和XGBoost实现DDoS流量分类 sFlow RT&Mininet流量采集与分类 杭电综合项目实践

Cyber-attack classification in the network traffic database using NSL-KDD dataset