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Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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An optimized anomaly detection framework in industrial control systems through grey wolf optimizer and autoencoder

Muhammad Muzamil Aslam1, Liyanage Chandratilak De Silva2, Rosyzie Anna Awg Haji Mohd Apong1

  • 1School of Digital Science, Universiti Brunei Darussalam, Gadong A, Bandar Seri Begawan, BE1410, Brunei Darussalam.

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Summary

This study introduces a new method for detecting anomalies in Industrial Control Systems (ICS) by combining the Grey Wolf Optimizer (GWO) and Autoencoders (AE). The optimized framework significantly improves detection accuracy and reduces errors.

Keywords:
Anomaly detectionCollaborative approach (GWO+AE)Industrial control systemSWaTSecurityWADI datasets

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Industrial Control Systems

Background:

  • Reliable Internet connectivity is crucial for Industrial Control Systems (ICS) real-time monitoring and anomaly detection.
  • Current anomaly detection methods in ICS face challenges like high computational complexity, dataset limitations, and high false-positive rates.

Purpose of the Study:

  • To develop a novel collaborative data processing framework for enhanced anomaly detection in ICS.
  • To integrate and optimize the Grey Wolf Optimizer (GWO) with Autoencoders (AE) for improved performance.

Main Methods:

  • The proposed approach optimizes GWO through enhanced prey selection, encircling, and initial population generation.
  • Autoencoder (AE) dropout functionality is improved for better model generalization.
  • The framework employs a two-stage process: GWO for feature selection and AE for anomaly detection.

Main Results:

  • Experimental validation on SWaT and WADI datasets showed superior performance compared to existing methods.
  • Significant improvements were observed in accuracy, precision, recall, and F1-score.
  • The model effectively identifies relevant features and reduces feature errors.

Conclusions:

  • The proposed GWO-AE framework demonstrates significant potential in addressing limitations of current ICS anomaly detection systems.
  • The approach offers a more accurate and reliable solution for real-time monitoring and anomaly detection in ICS environments.