AS-CNN is an approach proposed for enhancing anomaly detection in wireless networks, particularly focusing on addressing the vulnerabilities present in traditional methods. This project integrates Adaptive Synthetic Sampling (ADASYN) and a novel Convolutional Neural Network (CNN) architecture, termed Split-Convolution CNN (SPC-CNN), to achieve improved accuracy, detection rates, and reduced false alarm rates compared to conventional IDS models.

• ADASYN Integration: Balances the sample distribution by generating synthetic samples for minority classes, thus mitigating the bias towards frequent classes commonly observed in imbalanced datasets.

• SPC-CNN Architecture: Utilizes Split-Convolution Modules to enhance feature diversity and eliminate interchannel redundancy during model training. This architecture enables the extraction of multi-scale features from oversampled data, improving the model's representation capability.

• Performance Evaluation: The AS-CNN model is evaluated using the widely-used NSL-KDD dataset, encompassing various attack types. Evaluation metrics include Accuracy (ACC), Detection Rate (DR), and False Alarm Rate (FAR), providing insights into the model's effectiveness in detecting anomalies.

• KDDTrain
• KDDTest+
• KDDTest-21

• AS-CNN demonstrates superior performance compared to traditional CNN models, exhibiting higher accuracy, increased detection rates, and significantly reduced false alarm rates across different subsets of the NSL-KDD dataset.

• The model's robustness is particularly highlighted in its ability to detect minority attack classes such as Remote-to-Local (R2L) and User-to-Root (U2R) attacks, thereby enhancing cyber threat detection capabilities in wireless networks.

DR	ACC

• AS-CNN shows superior detection rates for minority attack classes.
• Other models show significantly lower detection rates for R2L and U2R attacks.
• AS-CNN’s robustness makes it reliable for cyber threat detection.

1. DoS: Denial of Service
2. Probe: Network Probe
3. R2L: Remote-to-Local
4. U2R: User-to-Root

To run the AS-CNN model and reproduce the results:

1. Clone the repository:

git clone https://github.com/rohzzn/nids.git

2. Navigate to the project directory:

cd nids

3. Install the required dependencies:

pip install -r requirements.txt

4. Execute the main script to train and evaluate the AS-CNN model

python main.py

This project is licensed under the MIT License - see the LICENSE file for details.