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Published October 7, 2024 | Version v1
Dataset Open

Dataset to Train Intrusion Detection Systems based on Machine Learning Models for Electrical Substations

  • 1. CIRCE

Description

DATASET

This dataset is part of the research work titled "A Dataset to Train Intrusion Detection Systems based on Machine Learning Models for Electrical Substations," which is currently awaiting approval for publication. The dataset has been meticulously curated to support the development and evaluation of machine learning models tailored for detecting cyber intrusions in the context of electrical substations. It is intended to facilitate research and advancements in cybersecurity for critical infrastructure, specifically focusing on real-world scenarios within electrical substation environments. We encourage its use for experimentation and benchmarking in related areas of study.

The following sections list the content of the dataset generated.

Data

  • raw
    • iec6180
      • attack-free-data
        • capture61850-attackfree.pcap (from real substation)
        • capture61850-attackfree_PTP.pcap
        • capture61850-attackfree_normalfault.pcap
      • attack-data
        • capture61850-floodattack_withfault.pcap
        • capture61850-floodattack_withoutfault.pcap
        • capture61850-fuzzyattack_withfault.pcap
        • capture61850-fuzzyattack_withoutfault.pcap
        • capture61850-replay.pcap
        • capture61850-ptpattack.pcap
    • iec104
      • attack-free-data
        • capture104-attackfree.pcap (from real substation)
      • attack-data
        • capture104-dosattack.pcap
        • capture104-floodattack.pcap
        • capture104-fuzzyattack.pcap
        • capture104-iec104starvationattack.pcap
        • capture104-mitmattack.pcap
        • capture104-ntpddosattack.pcap
        • capture104-portscanattack.pcap
  • processed
    • iec6180
      • attack-free-data
        • capture61850-attackfree.csv
        • capture61850-attackfree_PTP.csv
        • capture61850-attackfree_normalfault.csv
      • attack-data
        • capture61850-floodattack_withfault.csv
        • capture61850-floodattack_withoutfault.csv
        • capture61850-fuzzyattack_withfault.csv
        • capture61850-fuzzyattack_withoutfault.csv
        • capture61850-replay.csv
        • capture61850-ptpattack.csv
      • headers_iec61850[all].txt
    • iec104
      • attack-free-data
        • capture104-attackfree.csv
      • attack-data
        • capture104-dosattack.csv
        • capture104-floodattack.csv
        • capture104-fuzzyattack.csv
        • capture104-iec104starvationattack.csv
        • capture104-mitmattack.csv
        • capture104-ntpddosattack.csv
        • capture104-portscanattack.csv
      • headers_iec104[all].txt

 

Description

  • file type: it may be captured61850 or captured104 depending on whether it contains network captures of the protocol IEC61850 or IEC104.
  • attack: attack free (attackfree) or attack name is added to the file name.
  • function: optionally, if there are some details about functionality captured (normalfault) or specific protocol capture (PTP).
  • file extension: the type can be PCAP (network capture) or CSV (flow file).

 

Results

  • results
    • test1-iec104
      • model-test1-iec104.pkl
      • test1-iec104.log
    • test1-iec61850
      • model-test1-iec61850.pkl
      • test1-iec61850.log
    • test2-iec61850
      • model-test2-iec61850.pkl
      • test2-iec61850.log


Description

The outcomes of different test executions are available as follows:

  • test1-iec104: IEC 104 protocol for all attacks and attack free scenario
  • test1-iec61850: IEC 61850 protocol for fuzzy attack with fault injection and attack free scenario
  • test2-iec61850: IEC 61850 protocol for fuzzy attack normal operation and attack free scenario


Each test consists of the model results in Python pickle format (with a .pkl extension) and a detailed description of the execution conditions in an output log file (with a .log extension). 

Files

README.md

Files (1.2 GB)

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md5:ace90df8f4dd8c391796bc65a70ceb29
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md5:5d31fc6eb411fb6e791ab7da020f87e5
914.2 MB Download
md5:6f4516fbb9641f9937a3c78deb00695a
52.0 MB Download
md5:a9d31a93627d24a1e0e62204b5116c61
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Additional details

Funding

European Commission
eFORT - Establishment of a FramewORk for Transforming current EPES into a more resilient, reliable and secure system all over its value chain 101075665

Software

Repository URL
https://github.com/esguti/cybersecurity-datasets
Programming language
Shell , Python
Development Status
Active