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Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for

Onur Polat1, Muammer Türkoğlu2, Hüseyin Polat3

  • 1Department of Computer Engineering, Bingöl University, Bingöl 12000, Turkey.

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|February 10, 2024
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Summary
This summary is machine-generated.

This study introduces a multi-stage learning model to detect Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN)-based Supervisory Control and Data Acquisition (SCADA) systems, achieving 97.8% accuracy.

Keywords:
CNNDDoS attacksSCADASDNcritical infrastructurescyber pandemicmachine learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Industrial Control Systems

Background:

  • Traditional Supervisory Control and Data Acquisition (SCADA) systems face challenges in flexibility, scalability, and management due to conventional network structures.
  • Software-Defined Networking (SDN) offers potential solutions by separating control and data planes, but introduces new vulnerabilities, particularly against Distributed Denial of Service (DDoS) attacks targeting its centralized controller.
  • Effective detection of DDoS attacks is crucial to prevent severe disruptions in SDN-based SCADA environments.

Purpose of the Study:

  • To propose and evaluate a novel multi-stage learning model for the effective detection of DDoS attacks in SDN-based SCADA systems.
  • To address the security vulnerabilities introduced by integrating SDN into SCADA networks.
  • To enhance the resilience of critical industrial infrastructure against sophisticated cyber threats.

Main Methods:

  • Development of a multi-stage learning model combining a 1-dimensional Convolutional Neural Network (1D-CNN) and decision tree-based classification.
  • Creation of a new dataset featuring diverse attack scenarios within a specific experimental network topology for training and testing.
  • Experimental validation of the proposed model's performance in detecting DDoS attacks.

Main Results:

  • The proposed multi-stage learning model achieved a high accuracy rate of 97.8% in detecting DDoS attacks.
  • The model demonstrated effective identification of various attack scenarios within the SDN-based SCADA network.
  • Early detection capabilities were highlighted, enabling timely security measure implementation.

Conclusions:

  • The developed multi-stage learning model is highly effective for detecting DDoS attacks in SDN-based SCADA systems.
  • This approach offers a significant advancement in securing critical industrial control infrastructure.
  • The findings underscore the potential of advanced machine learning techniques for robust cybersecurity in industrial environments.