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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The Semantic Web facilitates data integration but faces growing cybersecurity challenges.
  • Traditional security methods are insufficient for complex, interconnected Semantic Web structures.
  • Securing linked data, ontologies, and network architectures is critical.

Purpose of the Study:

  • To propose a novel cybersecurity approach for the Semantic Web.
  • To enhance the resilience of the Semantic Web against evolving cyber threats.
  • To leverage nature-inspired algorithms for adaptive defense mechanisms.

Main Methods:

  • Utilized swarm optimization algorithms, inspired by insect behavior.
  • Implemented a distributed and adaptive defense mechanism.
  • Applied the approach to detect and mitigate threats in real-time.

Main Results:

  • Demonstrated significant improvement in network robustness against diverse attack scenarios.
  • Showcased effective protection for ontologies and data relationships.
  • Validated the ability to dynamically adapt to new cyber threats.

Conclusions:

  • Nature-inspired swarm optimization offers a robust solution for Semantic Web security.
  • The proposed strategy enables secure and reliable information exchange in distributed systems.
  • Adaptive defense mechanisms are key to addressing dynamic cyber threats.