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A dataset to train intrusion detection systems based on machine learning models for electrical substations.

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This study introduces a new dataset for electrical substations to train and test cybersecurity Intrusion Detection Systems (IDS). The data aids in developing machine learning models for enhanced substation security.

Keywords:
Critical infrastructureCybersecurityIEC104IEC60870-5-104IEC61850Testbed

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Area of Science:

  • Electrical Engineering
  • Cybersecurity
  • Computer Science

Background:

  • Increasing integration of Information and Communication Technology (ICT) in Operational Technology (OT) environments of electrical substations introduces significant cybersecurity vulnerabilities.
  • The need for robust cybersecurity measures is paramount to ensure the reliable operation of power distribution networks.

Purpose of the Study:

  • To present a comprehensive dataset of electrical substation network traffic for training and benchmarking machine learning-based Intrusion Detection Systems (IDS).
  • To provide a valuable resource for advancing cybersecurity in critical infrastructure.

Main Methods:

  • Collected and processed raw network captures and traffic flows from real electrical substations.
  • Included data on key substation protocols (IEC61850, IEC104, NTP, PTP) and simulated cyberattack traces.
  • Developed complementary tools for dataset creation and preprocessing to ensure standardization and reproducibility.

Main Results:

  • A diverse dataset comprising real and simulated substation network traffic, including normal operations and cyberattack scenarios.
  • The dataset is anonymized and filtered, ensuring privacy and suitability for machine learning model development.
  • Included tools facilitate consistent and reproducible data handling for cybersecurity research.

Conclusions:

  • The developed dataset addresses the critical need for high-quality, targeted data for tuning IDS in electrical substations.
  • This resource will contribute to the development of more effective machine learning models for substation cybersecurity.
  • Enhancing IDS capabilities is crucial for securing reliable power distribution networks against evolving cyber threats.