jumabek/net_intrusion_detection

Network intrusion detection with Machine Learning (Deep Learning) experiment : 1d-cnn, softmax, neural networks, convolution

Deep Learning based network intrusion detection in PyTorch

Net intrusion detection experiment for Final Project of DeepLearning class at Inha University.

Dataset homepage: https://www.unb.ca/cic/datasets/ids-2017.html

Contributions

1. Correct Evaluation Metric
2. Adressing data imblance
3. Benchmark results for different ML models
4. Running code for training/evaluating

Accompanying slides

https://docs.google.com/presentation/d/1Rjj1vF0hv8vSJWeDxk23nE4A4w3fv8tBdvsyIBpWTdU/edit?usp=sharing

Model Performance using K-Fold Cross-Validation

Classifier	5-Fold Balanced Accuracy
Content Linear Softmax	76.27
Neural Network with 3 dense layer	85.73
Neural Network with 5 dense layer	85.63
1D-CNN with 2conv 1fc layer	87.13
CNN with 5conv layer	87.16
Random Forest	80.09

Softmax

Please run the Softmax.ipynb

NN

Please run the NN.ipynb There are two NN architectures:

1. 'nn3' - 3 layers
2. 'nn5' - 5 layers

1D-CNN

Please run the CNN.ipynb There are two 1D-CNN architectures:

1. 'cnn2' - 2 conv layers
2. 'cnn5' - 5 conv layers