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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Federated learning with enhanced cryptographic security for vehicular cyber-physical systems.

Himanshi Babbar1, Shalli Rani1, Mohammad Shabaz2

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.

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Summary

This study introduces a novel federated learning design to enhance data security in vehicular cyber-physical systems (VCPS). The method protects sensitive data during decentralized training and aggregation, improving transportation safety and efficiency.

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Area of Science:

  • Cybersecurity
  • Intelligent Transportation Systems
  • Machine Learning

Background:

  • Vehicular cyber-physical systems (VCPS) are crucial for intelligent transportation systems (ITS), enabling features like self-driving vehicles and real-time traffic control.
  • The sensitive data handled by VCPS poses significant security risks, including threats to public safety and financial losses.
  • Existing security measures often struggle to balance robust protection with the performance demands of modern transportation networks.

Purpose of the Study:

  • To propose a novel federated learning framework designed to enhance data security within vehicular cyber-physical systems (VCPS).
  • To ensure data privacy during decentralized model training and secure aggregation of local models into a global model.
  • To address the critical need for robust security in evolving ITS infrastructure.

Main Methods:

  • Implemented a federated learning approach enabling decentralized model training on vehicles and roadside units (RSUs).
  • Integrated the Laplace method for adding noise to model updates, further protecting privacy during global model aggregation.
  • Established a collaborative framework involving vehicles, RSUs, and a central server to prevent information leaks.

Main Results:

  • The proposed federated learning design demonstrated superior performance compared to traditional cryptographic techniques on the CICIDS2017 dataset.
  • The method effectively preserved high levels of data confidentiality and system security.
  • Achieved these security enhancements without compromising computational efficiency or model accuracy.

Conclusions:

  • The novel federated learning framework offers a promising solution for securing sensitive data in VCPS.
  • This approach is essential for maintaining the integrity and reliability of intelligent transportation systems as they advance.
  • Implementing such sophisticated security measures will ultimately contribute to improved transportation safety and efficiency.