Optimizing Cloud-Integrated Computer Networks: Strategies for Enhanced Performance and Security

Abstract

This study proposes a unified, Python-based simulation framework to enhance the performance and security of cloud-integrated computer networks. The framework concurrently addresses two critical aspects, load balancing and intrusion detection, within a single reproducible environment. Using the CICIDS2017 dataset, a Random Forest classifier was used to detect a wide range of network attacks with high accuracy. To simulate realistic traffic behavior, synthetic data were generated for performance metrics such as latency, throughput, and packet loss. Load balancing is evaluated using round-robin and random assignment strategies across virtual servers, illustrating the trade-offs between uniformity and randomness in the request distribution. The experimental results demonstrated a classification accuracy of 99.79%, with precision and recall metrics supporting the robustness of the selected model. Feature importance analysis highlights the key indicators of anomalous traffic, and confusion matrices and precision-recall curves validate the detection performance. Additionally, the simulated network KPIs provide a scalable approximation of the Quality of Service under varying load scenarios. The proposed research is the only one that suggests an integrated and data-driven approach to fill the gap documented in previous studies, where network security and resource optimization are frequently studied independently. The proposed framework provides a convenient basis for additional scholarly and industrial investigations in the field of secure cloud networking.