This is the code for the paper: "AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection" (Infocom 2024)
Xinchen Zhang, Running Zhao, Zhihan Jiang, Zhicong Sun, Yulong Ding, Edith C.H. Ngai, Shuang-hua Yang.

The project is implemented using PyTorch and has been tested on the following hardware and software configuration:

• Ubuntu 20.04 Desktop
• NVIDIA GeForce RTX 3090 GPUs
• CUDA, version = 11.7
• PyTorch, version = 1.13.1
• Anaconda3

To install the necessary libraries and dependencies, run the following command:

pip install -r requirements.txt

We tested the effectiveness of our proposed method on the NSL-KDD and UNSW-NB15 datasets. Preprocessed versions of these datasets are provided in this repository, allowing for immediate execution. The continuous attributes have been normalized, and categorical attributes have been one-hot encoded.

Here is an example of how to start training:

python online_training.py --dataset unsw --epochs 800 --epoch_1 1 --flip_percent 0.05 --sample_interval 2784

If you find this code useful in your research, please cite:

@INPROCEEDINGS{zhang2024aoc,
  author={Zhang, Xinchen and Zhao, Running and Jiang, Zhihan and Sun, Zhicong and Ding, Yulong and Ngai, Edith C.H. and Yang, Shuang-Hua},
  booktitle={IEEE INFOCOM 2024 - IEEE Conference on Computer Communications}, 
  title={AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection}, 
  year={2024},
  volume={},
  number={},
  pages={581-590},
  keywords={Training;Decision making;Intrusion detection;Manuals;NSL-KDD;Feature extraction;Labeling;intrusion detection system;online learning;contrastive learning;Internet of Things},
  doi={10.1109/INFOCOM52122.2024.10621346}}