Machine Learning-Driven Threat Prediction and Self-Healing Security for Cloud-Native Enterprise Platforms
DOI:
https://doi.org/10.15662/IJARCST.2025.0806036Keywords:
Machine Learning, Threat Prediction, Self-Healing Security, Cloud-Native Computing, Cybersecurity, Anomaly Detection, Kubernetes, Intelligent Automation, Threat Intelligence, Zero TrustAbstract
Cloud-native enterprise platforms increasingly rely on containers, Kubernetes, microservices, APIs, serverless components, and distributed cloud infrastructure, creating dynamic attack surfaces that challenge conventional security mechanisms. Machine learning (ML) provides an opportunity to move beyond reactive defense by predicting emerging threats from behavioral, network, application, and infrastructure telemetry. This research proposes a machine learning-driven threat prediction and self-healing security framework for cloud-native enterprise platforms. The framework integrates continuous telemetry collection, feature engineering, anomaly detection, threat classification, risk scoring, and automated response mechanisms to identify potentially malicious activities before they develop into significant security incidents. Supervised and unsupervised learning techniques are combined to detect both known and previously unseen attack patterns, while an intelligent orchestration layer translates security predictions into controlled remediation actions. Self-healing mechanisms can isolate compromised workloads, rotate credentials, update security policies, restart unhealthy services, block suspicious traffic, and restore workloads from trusted configurations. Explainability mechanisms are incorporated to support security analysts in understanding why threats are predicted and why remediation actions are triggered. The proposed methodology emphasizes accuracy, low detection latency, resilience, scalability, and reduced operational dependency on manual intervention. The framework aims to establish an adaptive security architecture capable of continuously learning from cloud environments while improving enterprise cyber resilience.
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