CyberEcoGuard: Evolutionary algorithms and nature-mimetic defenses for enhancing network resilience in cloud infrastructures.
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Abstract
This research proposes CyberEcoGuard, a novel security framework that integrates evolutionary algorithms and nature-mimetic defense mechanisms to bolster cloud infrastructure resilience against ever-evolving cyber threats. The framework's efficacy is assessed through a combined approach of experimental simulations and real-world case studies. Evolutionary algorithms facilitate dynamic network configuration optimization, while nature-inspired defenses mimic the adaptive and self-healing characteristics of natural ecosystems. The findings reveal significant advancements in network resilience, including a 45% reduction in successful cyber-attacks, a 30% improvement in recovery times, and a 25% increase in threat detection accuracy compared to traditional security measures. These results underscore CyberEcoGuard's potential as a robust and scalable solution for real-time adaptive security in cloud environments. Furthermore, this work emphasizes the value of interdisciplinary approaches that bridge evolutionary biology and ecology principles with cybersecurity to tackle intricate challenges in contemporary network defense, ultimately contributing to the advancement of cloud security and offering valuable insights for the development of more resilient and adaptive security systems.
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