jiejaycao / encrypted-traffic-classification-models

SOTA-ETC-Methods-Pytorch

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Description

Replication of state-of-the-art Encrypted Traffic Classification methods based on Pytorch.

Dataset

Our encrypted traffic classification tasks are designed based on three of the most widely used datasets, including ISCX-VPN, ISCX-TOR and USTC-TFC, which can be obtained from Baidu Cloud or downloaded from the dataset source address for ease of use. (链接: https://pan.baidu.com/s/1AyMzsJGfc_iIBjL8hokvqA 提取码: cgv1)

Installation

Method list

Method Name	Paper Source
1DCNN	End-to-end encrypted traffic classification with one-dimensional convolution neural networks
TSCRNN	TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT
LSTM-Att	Identification of Encrypted Traffic Through Attention Mechanism Based Long Short Term Memory
MATEC	MATEC: A lightweight neural network for online encrypted traffic classification
DeepPacket	Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning
Datanet	Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway
SAM	Self-attentive deep learning method for online traffic classification and its interpretability
ET-BERT	ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification

encrypted-traffic-classification-models's Issues

lack of source file "utils.py"

build_dataset, build_iterator, get_time_dif