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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Federated Learning and EEL-Levy Optimization in CPS ShieldNet Fusion: A New Paradigm for Cyber-Physical Security.

Nalini Manogaran1, Yamini Bhavani Shankar2, Malarvizhi Nandagopal3

  • 1Department of Computer Science and Business Systems, S.A. Engineering College (Autonomous), Chennai 600077, Tamil Nadu, India.

Sensors (Basel, Switzerland)
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Summary

Cyber-physical systems (CPS) face evolving cyber threats. The CPS ShieldNet Fusion model enhances CPS security using federated learning and optimization, improving threat detection while preserving data privacy.

Keywords:
artificial intelligence (AI)cyber threatscyber–physical systems (CPSs)federated learningoptimizationsecurity

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Area of Science:

  • Cyber-physical Systems Security
  • Deep Learning Applications
  • Federated Learning

Background:

  • Cyber-physical systems (CPS) are increasingly vital, integrating industrial control, smart grids, and medical devices.
  • The complexity and interconnectedness of CPS make them vulnerable to sophisticated cyberattacks.
  • Existing deep learning solutions for CPS security face challenges in scalability, data privacy, and adaptability to dynamic environments.

Purpose of the Study:

  • To introduce CPS ShieldNet Fusion, a novel security framework designed to protect CPS from advanced cyber threats.
  • To develop a robust and scalable solution addressing limitations in current CPS cybersecurity approaches.
  • To enhance threat detection and response capabilities within CPS environments.

Main Methods:

  • Integration of Federated Residual Convolutional Network (FedRCNet) for decentralized, privacy-preserving model training.
  • Application of EEL-Levy Fusion Optimization (ELFO) to enhance the efficiency and effectiveness of threat detection.
  • Combination of FedRCNet and ELFO to create the comprehensive CPS ShieldNet Fusion model.

Main Results:

  • CPS ShieldNet Fusion demonstrated superior accuracy and effectiveness across multiple benchmark datasets (CICIoT-2023, Edge-IIoTset-2023, UNSW-NB).
  • The model achieved state-of-the-art performance in detecting diverse cyber threats in various CPS settings.
  • Decentralized training via federated learning successfully preserved data privacy.

Conclusions:

  • The CPS ShieldNet Fusion framework offers a significant advancement in securing cyber-physical systems against current and future threats.
  • The integration of federated learning and advanced optimization provides a scalable and privacy-preserving cybersecurity solution.
  • The proposed model holds substantial potential for enhancing the resilience and security of critical infrastructure and everyday technologies.