One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection
This repo contains the code needed to support the experiments performed in the paper "One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection". Note that this code contains dependencies on external services such as Weights and Biases and other python libraries.
Assuming all of the dependencies are in place such as an account on Weights and Biases one can execute experiments by each dataset or all at once.
To do this simply clone the repo, change to its directory, and execute:
./run_all_tests
This will download the data needed and execute what we considered a single experiment in the paper.
To do this, clone the repo, change to its directory, and execute:
./download_data
./run_<dataset to run>
where you replace with one of the dataset run scripts in the repo such as run_iot23_scenario_1.
After performing an experiment the resulting data can be found in the ./outputs/ directory.