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Towards Developing a Robust Intrusion Detection Model Using Hadoop-Spark and Data Augmentation for IoT Networks.

Ricardo Alejandro Manzano Sanchez1, Marzia Zaman2, Nishith Goel1

  • 1Cistech Limited, 201-203 Colonnade Rd, Nepean, ON K2E 7K3, Canada.

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Summary
This summary is machine-generated.

This study introduces a big data framework using Hadoop-Spark and Conditional Tabular Generative Adversarial Networks (CTGAN) to enhance anomaly detection in Internet of Things (IoT) networks, significantly improving intrusion detection accuracy for minority classes.

Keywords:
BoT-IoTCTGANIoT (internet of things) securitybig data frameworkhadoop-sparkimbalaced datasets

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Big Data Analytics

Background:

  • Anomaly detection and machine learning are crucial for intrusion detection systems in Internet of Things (IoT) networks.
  • Model robustness in these systems is challenged by data volume, quality, and class imbalance, particularly with exponentially increasing IoT network traffic.
  • Existing methods often struggle with the imbalanced nature of IoT datasets, impacting the detection of less frequent attacks.

Purpose of the Study:

  • To propose a novel framework for robust intrusion detection in IoT networks addressing data volume and class imbalance issues.
  • To leverage big data technologies like Hadoop-Spark for efficient training and testing of classification models.
  • To enhance the detection of minority attack classes within imbalanced datasets.

Main Methods:

  • Implemented a big data methodology using Hadoop-Spark for training and testing intrusion detection models on the entire BoT IoT dataset.
  • Employed a one-vs-rest strategy for multi-class and binary classification.
  • Utilized Conditional Tabular Generative Adversarial Networks (CTGAN) to generate synthetic data for minority classes, mitigating class imbalance.

Main Results:

  • Evaluated the performance of all available Hadoop-Spark algorithms regarding accuracy and processing time.
  • Demonstrated significant improvement in detecting minority classes, with the F1-score for the 'Theft' attack increasing from 42% to 99%.
  • The proposed framework outperformed existing models in intrusion detection accuracy and efficiency.

Conclusions:

  • The proposed Hadoop-Spark and CTGAN-based framework effectively addresses data volume and class imbalance challenges in IoT intrusion detection.
  • This approach significantly enhances the accuracy of detecting critical, low-frequency attacks.
  • The methodology offers a scalable and robust solution for securing rapidly evolving IoT environments.