Rasd

Overview

Rasd is a framework designed for semantic shift detection and adaptation in learning-based multi-class network intrusion detection systems. It comprises two main components:

Shift Detection Function

• Utilizes a centroid-based loss function to detect shifts.

Shift Adaptation Module

• Selects a representative subset of detected shift samples, approximating the entire distribution.
• Manually labels this subset to train a Pseudo-Labeler, which is then used to pseudo-label the remaining unselected samples.

For further details, please refer to the main paper.

Pre-requisites and requirements

Before running Rasd, ensure you have the necessary dependencies installed. These dependencies are listed in the 'requirements.txt' file. You can install them using the following command:

pip install -r requirements.txt

Here is the content of 'requirements.txt':

torch==2.0.1
numpy==1.25.0
pandas==1.5.3
scipy==1.10.1
sklearn==1.2.2
deap==1.4
optuna==3.2.0
tqdm==4.65.0

Models and Data

You can download the pre-trained models and the processed data from the following link:

Google Drive Folder

The contents of the download are as follows:

• RasdData.zip: Contains the processed data.
• RasdModels.zip: Contains the pre-trained models.

Download and extract these files into the main directory of Rasd (i.e., Rasd/). This will ensure that the data and models are properly organized and ready for use.

How to Use Rasd

To utilize Rasd with our settings, please follow these steps to set up the required datasets and run the framework.

Dataset Setup

First, download the datasets as mentioned in the Models and Data section. Ensure that the files are organized in the following directories:

• data/CICIDS2017/ for IDS2017
• data/CICIDS2018/ for IDS2018

You can directly download and unzip the datasets into the main directory of Rasd (i.e., Rasd/).

Running Rasd

To run Rasd, use the following command:

python Main.py

Command-Line Options

You can customize the execution using various command-line options:

Dataset Selection

Switch between datasets using the '--dataset_name' option.

Example:

python Main.py --dataset_name "CICIDS2017"

Options

"CICIDS2017" and "CICIDS2018"

Detection Threshold

Set the detection thresholds using the '--acceptance_err' option.

Example:

python Main.py --dataset_name "CICIDS2017" --acceptance_err 0.07

Options

0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, and 0.1

Training Mode

Use pre-trained models or train new models using the '--train_mode' option.

Example:

python Main.py --dataset_name "CICIDS2017" --acceptance_err 0.07 --train_mode "pre-train"

Options

"pre-train" and "train-new"

Mode of Operation

Select the operation mode (detection only or detection & adaptation) using the '--Mode' option.

Example:

python Main.py --dataset_name "CICIDS2017" --acceptance_err 0.07 --train_mode "pre-train" --Mode "Detection"

Options

"Detection" and "Both"

Detection Method

Choose the detection method using the '--Detection_Method' option.

Example:

python Main.py --dataset_name "CICIDS2017" --acceptance_err 0.07 --train_mode "pre-train" --Mode "Detection" --Detection_Method "Rasd"

Options

"Rasd", "LSL", and "CADE"

Selection Rate

Set the selection rate for building a subset for manual labeling using the '--selection_rate' option.

Example:

python Main.py --dataset_name "CICIDS2017" --acceptance_err 0.07 --train_mode "pre-train" --Mode "Detection" --Detection_Method "Rasd" --selection_rate 0.05

Options

0.01, 0.02, 0.03, 0.04, and 0.05

Selection Batch Size

Set the batch size for splitting the pool of detected samples using the '--selection_batch_size' option.

Example:

python Main.py --dataset_name "CICIDS2017" --acceptance_err 0.07 --train_mode "pre-train" --Mode "Detection" --Detection_Method "Rasd" --selection_rate 0.05 --selection_batch_size 3000

Options

3000, 2000, and 1000

Citation

@inproceedings{alotaibi24rasd,
  title={Rasd: Semantic Shift Detection and Adaptation for Network Intrusion Detection},
  author={Alotaibi, Fahad and Maffeis, Sergio},
  booktitle={the 39th International Conference on ICT Systems Security and Privacy Protection (SEC 2024)},
  pages={16--30},
  year={2024},
  organization={Springer}
}

Contact

If you have any questions or need further assistance, please feel free to reach out to me at any time:

• Email: f.alotaibi21@imperial.ac.uk
• Alternate Email: fahadalkarshmi@gmail.com