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Handling Class Imbalance in Continual Learning based Network Intrusion Detection System

In this work, we try handling infamous class imbalance problem frequently seen in intrusion detection datasets. Specifically, we study this problem under the application of continual learning (CL) to the intrusion detection. Under CL paradigm, the learning model will be more flexible to adapt to the newly seen attack pattern with minimal overhead.

Proposed System Model

ScreenShot

Datasets

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

Dataset contains 8 csv files, input to the datapreprocessing code. They are

  • Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv
  • Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv
  • Friday-WorkingHours-Morning.pcap_ISCX.csv
  • Monday-WorkingHours.pcap_ISCX.csv
  • Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv
  • Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv
  • Tuesday-WorkingHours.pcap_ISCX.csv
  • Wednesday-workingHours.pcap_ISCX.csv

Different Task Orders

In this work we formulated five different task orders. They are

  • Benign in task1 task_order
  • Benign in task2 task_order
  • Benign in task3 task_order
  • Benign in task4 task_order
  • Benign in task5 task_order

To execute different task order, assign the variable task_order to the one of the above task order.

Software setup details

We also used Google Colab during code build

Code execution

To run the code smoothly, follow the below steps in the same order

  • Ensure the correct software setup installation
  • Download and place all the csv files in the code directory
  • Run preprocess_dataset_ids17.py
  • Now you can run any of mlp or cnn architecture based code files
  • To run for different task order, follow steps in Different Task Orders section

Miscellaneous

Citation

@inbook{10.1145/3486001.3486231,
author = {Amalapuram, Suresh Kumar and Reddy, Thushara Tippi and Channappayya, Sumohana S. and Tamma, Bheemarjuna Reddy},
title = {On Handling Class Imbalance in Continual Learning Based Network Intrusion Detection Systems},
year = {2021},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3486001.3486231},
booktitle = {The First International Conference on AI-ML-Systems},
articleno = {11},
numpages = {7}
}

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