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<p>This text proposes an incrementally adaptive cloud native threat intelligence architecture to overcome static machine learning based intrusion detection system restrictions. Swarm evolved anomaly detection, quantum probabilistic learning, meta cognitive adversarial resilience, synaptic memory adaptation, and immune inspired response generation increase zero day threat detection and mitigations. Every component creates a tiered security model that may adjust its decision bounds as attack patterns evolve. The proposed system adjusts internally via distributed learning to high dimensional cloud traffic streams. The architecture prioritizes real time operating performance to reduce false alarms and resist perturbation driven adversarial attacks. The framework leverages long term threat memory to maintain detection performance while cloud parameters changes in process. In multi intrusion dataset studies, detection accuracy, adversarial resistance, and adaptation speed improve for the process. System behavior matches polymorphic malware and advanced persistent incursions in process. Large scale cloud infrastructure installations benefit from real time inference sets. Future cloud native cybersecurity might use bioinspired and incrementally adaptive intelligence for different scenarios. The framework allows autonomous, self evolving security architectures that adapt to changing threats. Empirical experimental results reveal a zero-day detection rate of 99.1% (baseline: 85.4%), false positive reductions to 2.1% (baseline: 6.7%), resilience against adversarial attacks of 96.7% (baseline: 79.2%), and better performance than conventional deep learning models. Such a bioinspired, constantly evolving framework will bring forth adaptation to highly sophisticated cyber threats, eliminating the regular agonies that manual retraining brings and promoting real-time cloud security most hit dramatically. By imitating biological intelligence as well as self-evolving mechanisms, our tactic sets a new standard in AI/ML driven resilience sets in cybersecurity.</p>

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Bioinspired and incremental learning-based cloud-native threat intelligence: AI/ML-enabled detection and mitigation of zero-day attacks

  • 期刊论文

  • 发表日期：

    2025年12月19日

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  • Nikita M. Sable,
  • Virendra K. Sharma,
  • Bhushan Manjre

摘要

This text proposes an incrementally adaptive cloud native threat intelligence architecture to overcome static machine learning based intrusion detection system restrictions. Swarm evolved anomaly detection, quantum probabilistic learning, meta cognitive adversarial resilience, synaptic memory adaptation, and immune inspired response generation increase zero day threat detection and mitigations. Every component creates a tiered security model that may adjust its decision bounds as attack patterns evolve. The proposed system adjusts internally via distributed learning to high dimensional cloud traffic streams. The architecture prioritizes real time operating performance to reduce false alarms and resist perturbation driven adversarial attacks. The framework leverages long term threat memory to maintain detection performance while cloud parameters changes in process. In multi intrusion dataset studies, detection accuracy, adversarial resistance, and adaptation speed improve for the process. System behavior matches polymorphic malware and advanced persistent incursions in process. Large scale cloud infrastructure installations benefit from real time inference sets. Future cloud native cybersecurity might use bioinspired and incrementally adaptive intelligence for different scenarios. The framework allows autonomous, self evolving security architectures that adapt to changing threats. Empirical experimental results reveal a zero-day detection rate of 99.1% (baseline: 85.4%), false positive reductions to 2.1% (baseline: 6.7%), resilience against adversarial attacks of 96.7% (baseline: 79.2%), and better performance than conventional deep learning models. Such a bioinspired, constantly evolving framework will bring forth adaptation to highly sophisticated cyber threats, eliminating the regular agonies that manual retraining brings and promoting real-time cloud security most hit dramatically. By imitating biological intelligence as well as self-evolving mechanisms, our tactic sets a new standard in AI/ML driven resilience sets in cybersecurity.