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Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical

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This survey reviews machine learning-based intrusion detection systems (IDSs) for critical infrastructure (CI) protection. It highlights challenges in detecting novel cyber threats and analyzes datasets for developing advanced security solutions.

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critical infrastructureindustrial control systemsintrusion detection systemsmachine learningsupervisory control and data acquisition

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Area of Science:

  • Cybersecurity
  • Industrial Control Systems (ICSs)
  • Machine Learning (ML)

Background:

  • Critical Infrastructure (CI) systems like ICSs, SCADA, and DCS are increasingly connected, expanding their attack surface.
  • Sophisticated cyber-attacks pose a significant threat to CI, necessitating advanced security measures beyond conventional systems.
  • Protecting CI is a national security priority due to its essential role in societal functions.

Purpose of the Study:

  • To compile and analyze the state-of-the-art in Intrusion Detection Systems (IDSs) utilizing Machine Learning (ML) for Critical Infrastructure (CI) protection.
  • To examine the security datasets employed for training ML models in CI cybersecurity.
  • To present recent research advancements in ML-based IDSs for CI over the last five years.

Main Methods:

  • Literature review and analysis of existing research on ML-based IDSs for CI.
  • Examination of security datasets used in training and evaluating ML models for CI cyber threats.
  • Synthesis of findings to identify trends, challenges, and future research directions.

Main Results:

  • ML techniques are increasingly integrated into IDSs to enhance threat detection capabilities for CI.
  • Challenges remain in detecting zero-day attacks and implementing practical ML-based solutions in real-world CI environments.
  • Analysis of various security datasets reveals their importance in the effectiveness of ML models.

Conclusions:

  • ML-powered IDSs offer promising advancements for securing CI against sophisticated cyber threats.
  • Further research is needed to address the detection of novel threats and the practical deployment of these systems.
  • The selection and quality of security datasets are crucial for the successful application of ML in CI cybersecurity.