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

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Jumabek/net_intrusion_detection

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Deep Learning based network intrusion detection in PyTorch

Net intrusion detection experiment for Final Project of DeepLearning class (2019) at Inha University.

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