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A False Sense Of Security? Revisiting The State Of Machine Learning-Based Industrial Intrusion Detection.pdf

2022-kus-iids-generalizability.pdf
Preview of A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection
🔗 Source: comsys.rwth-aachen.de
📊 Size: 1.93 MB
📄 Pages: 12 pages
⬇️ Downloads: 61

Summary

Researchers evaluated machine learning-based industrial intrusion detection systems, which promise to detect novel attacks by modeling expected system behavior. However, these systems are typically trained on both benign traffic and attacks, then evaluated against the same type of attack used for training, leaving their real-world performance on unknown attacks unclear. The study developed an evaluation methodology and examined multiple approaches, finding that detection rates dropped to between 3.2% and 14.7% for some types of unknown attacks, highlighting a false sense of security and the need for further research to ensure clarity on their ability to detect unknown attacks.

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Description

Researchers evaluated machine learning-based industrial intrusion detection systems, which promise to detect novel attacks by modeling expected system...

Technical Information

  • File Format: PDF
  • File Size: 1.93 MB
  • Pages: 12
  • Language: EN
  • Total Downloads: 61
  • Last Updated: 5 months ago

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This PDF document about A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection.

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