Network-Intrusion-Detection-Using-Deep-Learning

Loosely based on research paper A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach https://doi.org/10.1016/j.neucom.2019.11.016

Blog of this Project

• Network Intrusion Detection using Deep Learning on Medium.com

Repository Structure

Network-Intrusion-Detection-Using-Machine-Learning

• Datasets

  • bin_data.csv - CSV Dataset file for Binary Classification
  • multi_data.csv - CSV Dataset file for Multi-class Classification
  • KDDTrain+.txt - Original Dataset downloaded

• Labels

  • le1_classes.npy - Numpy file for ndarray containing Binary Labels
  • le2_classes.npy - Numpy file for ndarray containing Multi-class Labels

• Models

  • ae_binary.json - Trained Auto Encoder Model JSON File for Binary Classification
  • ae_multi.json - Trained Auto Encoder Model JSON File for Multi-class Classification
  • knn_binary.pkl - Trained K-Nearest-Neighbor Model Pickle File for Binary Classification
  • knn_multi.pkl - Trained K-Nearest-Neighbor Model Pickle File for Multi-class Classification
  • lda_binary.pkl - Trained Linear Discriminant Analysis Model Pickle File for Binary Classification
  • lda_multi.pkl - Trained Linear Discriminant Analysis Model Pickle File for Multi-class Classification
  • lst_binary.json - Trained Long Short-Term Memory Model JSON File for Binary Classification
  • lsvm_binary.pkl - Trained Linear Support Vector Machine Model Pickle File for Binary Classification
  • lsvm_multi.pkl - Trained Linear Support Vector Machine Model Pickle File for Multi-class Classification
  • mlp_binary.json - Trained Multi Layer Perceptron Model JSON File for Binary Classification
  • mlp_multi.json - Trained Multi Layer Perceptron Model JSON File for Multi-class Classification
  • qda_binary.pkl - Trained Quadratic Discriminant Analysis Model Pickle File for Binary Classification
  • qda_multi.pkl - Trained Quadratic Discriminant Analysis Model Pickle File for Multi-class Classification
  • qsvm_binary.pkl - Trained Quadratic SUpport Vector Machine Model Pickle File for Binary Classification
  • qsvm_multi.pkl - Trained Quadratic Support Vector Machine Model Pickle File for Multi-class Classification

• Weights

  • ae_binary.h5 - Model weights of Auto Encoder Model for Binary Classification
  • ae_multi.h5 - Model weights of Auto Encoder Model for Multi-class Classification
  • lst_binary.h5 - Model weights of Long Short-Term Memory Model for Binary Classification
  • mlp_binary.h5 - Model weights of Multi Layer Perceptron Model for Binary Classification
  • mlp_multi.h5 - Model weights of Multi Layer Perceptron Model for Multi-class Classification

• Plots

  • Pie_chart_binary.png - Pie chart of Binary Classification
  • Pie_chart_multi.png - Pie chart of Multi-class Classification
  • ae_binary.png - Auto Encoder Model Summary for Binary Classification
  • ae_binary_accuracy.png - Auto Encoder Accuracy Plot for Binary Classification
  • ae_binary_loss.png - Auto Encoder Loss Plot for Binary Classification
  • ae_multi.png - Auto Encoder Model Summary for Multi-class Classification
  • ae_multi_accuracy.png - Auto Encoder Accuracy Plot for Multi-class Classification
  • ae_multi_loss.png - Auto Encoder Loss Plot for Multi-class Classification
  • lstm_binary.png - Long Short-Term Memory Model Summary for Binary Classification
  • lstm_binary_accuracy.png - Long Short-Term Memory Accuracy Plot for Binary Classification
  • lstm_binary_loss.png - Long Short-Term Memory Loss Plot for Binary Classification
  • mlp_binary.png - Multi Layer Perceptron Model Summary for Binary Classification
  • mlp_binary_accuracy.png - Multi Layer Perceptron Accuracy Plot for Binary Classification
  • mlp_binary_loss.png - Multi Layer Perceptron Loss Plot for Binary Classification
  • mlp_multi.png - Multi Layer Perceptron Model Summary for Multi-class Classification
  • mlp_multi_accuracy.png - Multi Layer Perceptron Accuracy Plot for Multi-class Classification
  • mlp_multi_loss.png - Multi Layer Perceptron Loss Plot for Multi-class Classification

• Classifiers_NSL-KDD.ipynb - Machine Learning Classifiers IPYNB file

• Data_Preprocessing_NSL-KDD.ipynb - Data Preprocessing IPYNB File

• Intrusion_Detection.ipynb - Combined IPYNB File

Dataset

The NSL-KDD dataset from the Canadian Institute for Cybersecurity (updated version of the original KDD Cup 1999 Data (KDD99) https://www.unb.ca/cic/datasets/nsl.html

Prerequisites

• Keras
• Sklearn
• Pandas
• Numpy
• Matplotlib
• Pickle

Running the Notebook

The notebook can be run on

• Google Colaboratory
• Jupyter Notebook

Instructions

• To run the code, user must have the required Dataset on their system or programming environment.
• Upload the notebook and dataset on Jupyter Notebook or Google Colaboratory.
• Click on the file with .ipynb extension to open the notebook. To run complete code at once press Ctrl + F9
• To run any specific segment of code, select that code cell and press Shift+Enter or Ctrl+Shift+Enter

Caution - The code should be executed in the given order for best results without encountering any errors.

Citation

• Cosimo Ieracitano, Ahsan Adeel, Francesco Carlo Morabito, Amir Hussain, A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach, Neurocomputing (2019), doi: https://doi.org/10.1016/j.neucom.2019.11.016

• The NSL-KDD dataset from the Canadian Institute for Cybersecurity (updated version of the original KDD Cup 1999 Data (KDD99) https://www.unb.ca/cic/datasets/nsl.html