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Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems.

Saadin Oyucu1, Onur Polat2, Muammer Türkoğlu3

  • 1Department of Computer Engineering, Adıyaman University, Adıyaman 02040, Turkey.

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Summary
This summary is machine-generated.

This study introduces a Decision Tree-based Ensemble Learning method to detect Distributed Denial of Service (DDoS) attacks in Software Defined Networking (SDN)-based Supervisory Control and Data Acquisition (SCADA) systems. The proposed technique enhances cybersecurity for renewable energy management.

Keywords:
CPESDDoS attackSCADASDNrenewable energysmart grids

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Area of Science:

  • Cybersecurity in energy systems
  • Machine Learning for network security
  • Renewable energy infrastructure management

Background:

  • Supervisory Control and Data Acquisition (SCADA) systems are vital for renewable energy, but traditional infrastructures face scaling and management challenges.
  • Integrating Software Defined Networking (SDN) with SCADA offers benefits but increases cybersecurity risks, especially from Distributed Denial of Service (DDoS) attacks.
  • Cyber-physical energy systems (CPES) require robust security measures against threats that disrupt energy resources and increase operational costs.

Purpose of the Study:

  • To propose an effective intrusion detection system for SDN-based SCADA systems against DDoS attacks.
  • To develop a Decision Tree-based Ensemble Learning technique for accurate detection of DDoS traffic.
  • To enhance the security and reliability of renewable energy management systems.

Main Methods:

  • A Decision Tree-based Ensemble Learning technique was employed to distinguish between normal and DDoS attack traffic.
  • Feature selection and hyperparameter tuning were utilized to optimize the ensemble models' performance.
  • Normal and DDoS attack traffic data were collected and analyzed using a simulated experimental network topology.

Main Results:

  • The proposed ensemble learning models demonstrated accurate detection of DDoS attacks in SDN-based SCADA systems.
  • Feature selection, model ensembling, and hyperparameter tuning significantly improved the accuracy and performance of the detection models.
  • The study confirmed the effectiveness of the machine learning approach in identifying malicious traffic.

Conclusions:

  • The Decision Tree-based Ensemble Learning technique provides an effective solution for detecting DDoS attacks in SDN-based SCADA systems.
  • Optimized machine learning models are crucial for enhancing the cybersecurity of cyber-physical energy systems.
  • This research contributes to securing renewable energy infrastructure against sophisticated cyber threats.