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Correlation-Based Anomaly Detection in Industrial Control Systems.

Zahra Jadidi1,2, Shantanu Pal3, Mukhtar Hussain1

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Summary
This summary is machine-generated.

This study introduces a new system-wide anomaly detection solution for Industrial Control Systems (ICSs). It uses recurrent neural networks and correlation analysis to detect and predict cyber-attacks across entire networks.

Keywords:
anomaly detectioncorrelation analysiscyber attacksindustrial control systemsrecurrent neural networks

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Area of Science:

  • Cybersecurity
  • Industrial Control Systems (ICSs)
  • Network Anomaly Detection

Background:

  • Industrial Control Systems (ICSs) are increasingly connected to the internet, expanding their capabilities but also exposing them to cyber-attacks.
  • Cyber-attacks on ICS networks are complex, involving multiple devices and sequential actions, unlike traditional IT network attacks.
  • Current anomaly detection systems often fail to detect these complex attacks due to their focus on local analysis, missing cross-device correlations and temporal attack progression.

Purpose of the Study:

  • To develop a system-wide anomaly detection solution for ICS networks that addresses the limitations of local analysis.
  • To effectively detect complex, multi-device cyber-attacks within ICS environments.
  • To predict future attack actions, providing valuable insights for security analysts to enhance network defenses.

Main Methods:

  • A two-layer analysis system combining recurrent neural networks (RNNs) with correlation analysis techniques.
  • The first layer focuses on detecting anomalies and potential attacks across the network.
  • The second layer analyzes detected attacks to predict subsequent actions, offering a predictive security capability.

Main Results:

  • Proof-of-concept implementation successfully validated using two real-world ICS datasets: SWaT and Power System Attack.
  • The proposed solution demonstrated effective detection of anomalies and cyber-attacks on an entire ICS network scale.
  • The system-wide approach proved superior to local analysis in identifying correlated, sequential attack patterns.

Conclusions:

  • The developed system-wide anomaly detection solution effectively addresses the challenges of cybersecurity in interconnected ICS environments.
  • The combination of RNNs and correlation analysis provides a robust method for detecting and predicting sophisticated cyber-attacks.
  • This approach enhances the security posture of ICS networks by offering comprehensive, network-wide threat visibility and predictive insights.