Data representation for IoT traffic
Project description
Purpose
"Comparison of data representation for outlier detection in IOT"
1. Requirements (pip3 freeze > requirements.txt)
python==3.7.4
seaborn==0.9.0
scipy==1.3.1
matplotlib==3.1.1
scapy==2.4.3
pandas==0.25.1
pyod==0.7.4
numpy==1.17.0
umap_learn==0.3.10
scikit_learn==0.21.3
umap==0.1.1
keras==2.3.1 # for autoencoder
tensorflow==2.0.0
2. Project Directory Structure
iot_outlier_src: source root directory (set 'iot_outlier_src' as sources root)
|- input_data: raw data
if any file is more than 100MB, please do not store it at here
|- output_data: results
figures: store roc and auc
model_dumpling: store models to disk
...
|- data_process:
pcap2features
features2dataset
|- detector
gmm
ocsvm
|- utils
toolkit to preprocess input data, such as 'load data', 'normalization data'
visualization: plot data to visualize
..
|- log: use to log middle or tmp results.
...
|-legacy: backup
...
Main
main_featcomp-class.py
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tests
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