Nature-inspired swarm optimization paradigms for securing semantic web frameworks against DDoS attacks: a...

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.