README.md

HaUI Intrusion Detection

Paper - https://ieeexplore.ieee.org/document/10079326

CNN architecture follow:

Quick Start

Install lib

git clone https://github.com/DoManhQuang/intrusion-detection.git
cd intrusion-detection
pip install -r setup.txt

Train Deep Model

python tools/dl-train.py \
-train "./dataset/full_data_std.data" \
-test "./dataset/full_data_test_std.data" \
-v "std-v1.0-relu" -ep 1000 -bsize 64 -verbose 1 \
--result_path "./runs/results" \
--training_path "./runs/training" \
-activation_block "relu" \
-status_ckpt True -status_early_stop True \
-name "model-relu-std-v1.0"

Train Machine Model

python tools/ml-train.py -v "ml-tree-norm-v1" \
-train "./dataset/full_data_norm.data" \
-test "./dataset/full_data_test_norm.data" \
--result_path "./runs/results" \
--training_path "./runs/training" \
-name "tree"

Structure as follows

├── intrusion-detection
│   ├── core
│   │   ├── deep_model.py
│   │   └── machine_model.py
│   │   └── utils.py
│   ├── dataset
│   │   ├── train.data
│   │   └── test.data
│   ├── docs
│   ├── tools
│   │   ├── dl-train.py
│   │   ├── ml-train.py
│   ├── LICENSE
│   ├── README.md

Cite

@INPROCEEDINGS{10079326,
  author={Vu, Viet-Thang and Thi, Thanh Quyen Bui and Gan, Hong-Seng and Vu, Viet-Vu and Quang, Do Manh and Duc, Vu Thanh and Pham, Dinh-Lam},
  booktitle={2023 25th International Conference on Advanced Communication Technology (ICACT)}, 
  title={Activation functions for deep learning: an application for rare attack detection in wireless local area network (WLAN)}, 
  year={2023},
  volume={},
  number={},
  pages={59-64},
  doi={10.23919/ICACT56868.2023.10079326}}