This repository provides the original PyTorch implementation of the TrustGeo framework.
The code was tested with python 3.8.13, PyTorch 1.12.1, cudatoolkit 11.6.0, and cudnn 7.6.5. Install the dependencies via Anaconda:
# create virtual environment
conda create --name TrustGeo python=3.8.13
# activate environment
conda activate TrustGeo
# install pytorch & cudatoolkit
conda install pytorch torchvision torchaudio cudatoolkit=11.6 -c pytorch -c conda-forge
# install other requirements
conda install numpy pandas
pip install scikit-learn
# Open the "TrustGeo" folder
cd TrustGeo
# data preprocess (executing IP clustering).
python preprocess.py --dataset "New_York"
python preprocess.py --dataset "Los_Angeles"
python preprocess.py --dataset "Shanghai"
# run the model TrustGeo
python main.py --dataset "New_York" --lr 5e-3 --dim_in 30 --lambda1 7e-3
python main.py --dataset "Los_Angeles" --lr 3e-3 --dim_in 30 --lambda1 7e-3
python main.py --dataset "Shanghai" --lr 0.0015 --dim_in 51 --lambda1 1e-3
# load the checkpoint and then test
python test.py --dataset "New_York" --lr 5e-3 --dim_in 30 --lambda1 7e-3 --load_epoch 400
python test.py --dataset "Los_Angeles" --lr 3e-3 --dim_in 30 --lambda1 7e-3 --load_epoch 400
python test.py --dataset "Shanghai" --lr 0.0015 --dim_in 51 --lambda1 1e-3 --load_epoch 200
| Hyperparameter | Description |
|---|---|
| model_name | the name of model |
| dataset | the dataset used by main.py |
| lambda | the trade-off coefficient of loss function |
| lr | learning rate |
| harved_epoch | when how many consecutive epochs the performance does not increase, the learning rate is halved |
| early_stop_epoch | when how many consecutive epochs the performance does not increase, the training stops. |
| saved_epoch | how many epochs to save checkpoint for the testing |
| seed | the random number seed used for parameter initialization during training |
| dim_in | the dimension of input data |
└── TrustGeo
├── datasets # Contains three large-scale real-world street-level IP geolocation datasets.
│ |── New_York # Street-level IP geolocation dataset collected from New York City including 91,808 IP addresses.
│ |── Los_Angeles # Street-level IP geolocation dataset collected from Los Angeles including 92,804 IP addresses.
│ |── Shanghai # Street-level IP geolocation dataset collected from Shanghai including 126,258 IP addresses.
├── lib # Contains model implementation files
│ |── layers.py # The code of the attention mechanism.
│ |── model.py # The core source code of the proposed TrustGeo
│ |── sublayers.py # The support file for layer.py
│ |── utils.py # Auxiliary functions, including the code of view fusion
├── asset # Contains saved checkpoints and logs when running the model
│ |── log # Contains logs when running the model
│ |── model # Contains the saved checkpoints
├── preprocess.py # Preprocess dataset and execute IP clustering for the model running
├── main.py # Run model for training and test
├── test.py # Load checkpoint and then test
└── README.mdThe "datasets" folder contains three subfolders corresponding to three large-scale real-world street-level IP geolocation datasets collected from New York City, Los Angeles, and Shanghai. There are three files in each subfolder:
- data.csv # features (including attribute knowledge and network measurements) and labels (longitude and latitude) for street-level IP geolocation
- ip.csv # IP addresses
- last_traceroute.csv # last four routers and corresponding delays for efficient IP host clustering
The detailed columns and description of data.csv in the New York dataset are as follows:
| Column Name | Data Description |
|---|---|
| ip | The IPv4 address |
| as_mult_info | The ID of the autonomous system where IP locates |
| country | The country where the IP locates |
| prov_cn_name | The state/province where the IP locates |
| city | The city where the IP locates |
| isp | The Internet Service Provider of the IP |
| vp900/901/..._ping_delay_time | The ping delay from probing hosts "vp900/901/..." to the IP host |
| vp900/901/..._trace | The traceroute list from probing hosts "vp900/901/..." to the IP host |
| vp900/901/..._tr_steps | #steps of the traceroute from probing hosts "vp900/901/..." to the IP host |
| vp900/901/..._last_router_delay | The delay from the last router to the IP host in the traceroute list from probing hosts "vp900/901/..." |
| vp900/901/..._total_delay | The total delay from probing hosts "vp900/901/..." to the IP host |
| longitude | The longitude of the IP (as the label) |
| latitude | The latitude of the IP host (as the label) |
PS: The detailed columns and description of data.csv in the other two datasets are similar to the New York dataset.
If you find the code useful for your research, please consider citing
@inproceedings{tai2023trustgeo,
title = {TrustGeo: Uncertainty-Aware Dynamic Graph Learning for Trustworthy IP Geolocation},
author = {Tai, Wenxin and Chen, Bin and Zhou, Fan and Zhong, Ting and Trajcevski, Goce and Wang, Yong and Chen, Kai},
booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
year = {2023}
}
If you have any questions about this code or the paper, feel free to contact us: wxtai AT outlook.com or binchen4110 AT gmail.com