International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Cloud computing has rapidly evolved into the foundational infrastructure for modern digital services, enabling organizations to deploy applications with unprecedented scalability, elasticity, and cost efficiency. However, the dynamic and distributed nature of cloud-native environments introduces complex security challenges that traditional intrusion detection systems are ill-equipped to address. Conventional IDS solutions depend on static network boundaries, deep packet inspection, and predefined attack signatures, all of which are increasingly ineffective in cloud environments dominated by encrypted traffic, ephemeral workloads, and software-defined networking.
This study presents an extensive analysis of cloud-native intrusion detection systems that utilize Virtual Private Cloud (VPC) Flow Logs in conjunction with ensemble learning techniques. VPC Flow Logs provide scalable, lightweight, and privacy-preserving network traffic metadata, making them suitable for large-scale cloud monitoring. Ensemble learning methods combine multiple machine learning classifiers to enhance detection accuracy, reduce false positives, and improve robustness against evolving cyber threats. This paper systematically reviews existing research, explores architectural designs, analyzes detection methodologies, evaluates benefits and limitations, and discusses future research directions. The study demonstrates that ensemble-based intrusion detection using VPC Flow Logs offers a practical and effective solution for securing modern cloud infrastructures.

Keywords: Cloud Security, Intrusion Detection System, VPC Flow Logs, Ensemble Learning, Machine Learning, Cloud-Native Architecture.

Downloads: | DOI: 10.17148/IJARCCE.2026.1511231