RIPGeo框架代码构成

# RIPGeo     This folder provides a reference implementation of **RIPGeo**, as described in the paper: "RIPGeo: Robust Street-Level IP Geolocation". ## Basic Usage ### Requirements 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](https://www.anaconda.com/): ```shell # create virtual environment conda create --name RIPGeo python=3.8.13 # activate environment conda activate RIPGeo # 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 ``` ### Run the code ```shell # Open the "RIPGeo" folder cd RIPGeo # 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 RIPGeo python main.py --dataset "New_York" --dim_in 30 --lr 2e-3 --saved_epoch 100 python main.py --dataset "Los_Angeles" --dim_in 30 --lr 2e-3 --saved_epoch 100 python main.py --dataset "Shanghai" --dim_in 51 --lr 1e-3 --saved_epoch 70 # load the checkpoint and then test python test.py --dataset "New_York" --dim_in 30 --lr 2e-3 --load_epoch 100 python test.py --dataset "Los_Angeles" --dim_in 30 --lr 2e-3 --load_epoch 100 python test.py --dataset "Shanghai" --dim_in 51 --lr 1e-3 --load_epoch 70 ``` ## The description of hyperparameters used in main.py | Hyperparameter | Description | | :--------------- | ------------------------------------------------------------ | | seed | the random number seed used for parameter initialization during training | | model_name | the name of model | | dataset | the dataset used by main.py | | lambda_1 | the trade-off coefficient of data perturbation in loss function | | lambda_2 | the trade-off coefficient of parameter perturbation in 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 | | dim_in | the dimension of input data | | dim_med | the dimension of middle layers | | dim_z | the dimension of vector representation | | eta | magnitude of data disturbance | | zeta | magnitude of parameter disturbance | | step | times of gradient ascent in a single parameter disturbance | | mu | inner learning rate of parameter disturbance | | c_mlp | when predicting if use collaborative_mlp or not | | epoch_threshold | when we start adding perturbation both in data and parameter | ## Folder Structure ```tex └── RIPGeo ├── 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 proposed RIPGeo │ |── sublayers.py # The support file for layer.py │ |── utils.py # Auxiliary functions ├── 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 the for model running ├── main.py # Run model for training and test ├── test.py # Load checkpoint and then test └── README.md ``` ## Dataset Information The "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 New York dataset are as follows: #### New York | 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 label) | | latitude | The latitude of the IP host (as label) | PS: The detailed columns and description of data.csv in other two datasets are similar to New York dataset's.