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Artificial intelligence based optimization with deep learning model for blockchain enabled intrusion detection in CPS

Romany F Mansour1

  • 1Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt. romanyf@sci.nvu.edu.eg.

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Summary
This summary is machine-generated.

This study introduces a novel AI-driven intrusion detection system for cyber-physical systems (CPS). The PRO-DLBIDCPS technique enhances security using deep learning and blockchain, effectively identifying network threats.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Cyber-physical systems (CPS) are critical infrastructure, but their increasing use poses significant security challenges.
  • Intrusion detection systems (IDS) are essential for identifying network intrusions in CPS environments.
  • Advancements in AI and deep learning offer potential for designing effective IDS models.

Purpose of the Study:

  • To develop a novel Deep Learning Model for Blockchain Enabled Intrusion Detection in Cyber-Physical Systems (CPS).
  • To enhance intrusion detection efficiency through advanced feature selection and optimization techniques.
  • To integrate blockchain technology for improved security in CPS environments.

Main Methods:

  • Proposed a Poor and Rich Optimization with Deep Learning Model for Blockchain Enabled Intrusion Detection in CPS Environment (PRO-DLBIDCPS).
  • Employed Adaptive Harmony Search Algorithm (AHSA) for feature selection to address dimensionality.
  • Utilized an attention-based bi-directional gated recurrent neural network (ABi-GRNN) for intrusion detection and classification.
  • Optimized ABi-GRNN hyperparameters using the Poor and Rich Optimization (PRO) algorithm.
  • Integrated blockchain technology to bolster CPS security.

Main Results:

  • The PRO-DLBIDCPS technique demonstrated superior performance in intrusion detection and classification.
  • Feature selection using AHSA effectively reduced dimensionality and improved model efficiency.
  • Hyperparameter optimization via PRO enhanced the detection accuracy of the ABi-GRNN model.
  • Simulations on benchmark datasets confirmed the effectiveness of the proposed technique.

Conclusions:

  • The PRO-DLBIDCPS technique offers a robust solution for intrusion detection in CPS.
  • The integration of AI, deep learning, and blockchain significantly enhances CPS security.
  • The study highlights the potential of metaheuristic algorithms in optimizing IDS for complex environments.