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Neural Network Method of Analysing Sensor Data to Prevent Illegal Cyberattacks.

Serhii Vladov1, Vladimir Jotsov2,3, Anatoliy Sachenko4,5

  • 1Department of Scientific Activity Organization, Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
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This study introduces a new method using a modified Long Short-Term Memory (LSTM) network to detect cyberattacks on critical infrastructure sensor data. The approach effectively predicts normal behavior and identifies anomalies, achieving high accuracy in preventing attacks.

Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning

Background:

  • Critical infrastructure relies heavily on sensor devices, increasing vulnerability to cyberattacks like data forgery and denial of service.
  • Ensuring the security of these sensor networks is a growing challenge due to the rapid expansion of connected devices.

Purpose of the Study:

  • To develop an effective method for analyzing sensor data to prevent cyberattacks on critical infrastructure.
  • To enhance the security of sensor networks against various malicious activities.

Main Methods:

  • Utilized a modified Long Short-Term Memory (LSTM) network for predicting normal sensor data patterns.
  • Implemented anomaly detection by analyzing residual values between predicted and actual data.
  • Employed a hybrid approach combining predictive modeling with statistical deviation analysis.
Keywords:
cyber policecyberattacksloss functionneural networkresidualssensor systemsensory data

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Main Results:

  • Achieved high accuracy in predicting normal sensor data with an F1 score of 0.90.
  • Demonstrated high sensitivity to data changes through anomaly detection via residual analysis.
  • Attained a precision of 0.92 for attack detection, with overall accuracy up to 92% and recall up to 89% (AUC = 0.94).

Conclusions:

  • The developed method effectively protects critical infrastructure facilities from cyberattacks, even with limited computing resources.
  • The hybrid approach offers real-time efficiency and minimal computational costs for robust cybersecurity.
  • The method shows significant promise for enhancing the security posture of sensor networks in critical systems.