Cloud-Native Enterprise Architectures with Machine Learning for Real-Time Security Monitoring and Anomaly Detection
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605019Keywords:
Cloud-Native Architecture, Machine Learning, Real-Time Security Monitoring, Anomaly Detection, Cybersecurity, Threat Detection, Cloud SecurityAbstract
Cloud-native enterprise architectures have transformed the way organizations design, deploy, and operate digital services. Modern enterprises increasingly rely on containers, microservices, serverless functions, APIs, software-defined networks, managed databases, and elastic cloud infrastructure to deliver applications at scale. These technologies provide flexibility and rapid deployment, but they also create highly distributed environments in which security events can emerge across numerous interconnected components. Conventional security monitoring approaches that depend primarily on predefined signatures, static thresholds, and manual investigation may struggle to process the volume and complexity of telemetry generated by these environments. Machine learning offers an opportunity to complement conventional security mechanisms by learning patterns from network traffic, application behavior, authentication events, system logs, and resource utilization. By identifying deviations from established behavioral patterns, machine-learning models can support real-time anomaly detection and help security teams investigate potentially malicious activity more rapidly. The integration of machine learning with cloud-native architectures requires more than simply deploying an anomaly-detection model. Cloud environments are dynamic, meaning that legitimate system behavior can change because of workload scaling, software releases, infrastructure migration, changing user activity, and automated orchestration. An anomaly-detection system that interprets every deviation as a security event can therefore generate large numbers of false positives
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