Predictive Cyber-Resilience: AI-Powered Self-Defending Microservices for Zero-Downtime Security
DOI:
https://doi.org/10.32628/CSEIT25112391Keywords:
Autonomous Cybersecurity, Threat Mitigation, Zero-Downtime Security, Anomaly Detection, Synthetic Adversarial Networks, Federated Learning, Microservices Security, Predictive Cyber-Resilience, Ai Security, CybersecurityAbstract
Cyberattacks on distributed microservices have become more elusive, and it is not enough to only monitor for these microservices but rather an active defense is necessary. In this paper, we present Predictive Cyber-Resilience (PCR), an AI-driven self-defending security framework that secures both cloud-native and edge environments. PCR uses Preemptive Cyber response for predicting and neutralizing threats before their manifestation using federated learning, synthetic adversarial networks (SANs) and anomaly detection and reducing breaches by 90%. Different from traditional approaches, PCR adapts dynamically the human intervention to the changes in the attack vectors Working in conjunction with service meshes and multi-cloud platforms, PCR delivers a no-downtime security adaptation that enables a paradigm shift toward autonomous cybersecurity and AI-driven threat mitigation.
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Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.