Mathematical Algorithm-Based Intrusion Detection for Resilient Cloud VMs

Abstract

The exponential growth of cloud adoption has intensified the need for resilient virtual-machine (VM) architectures capable of resisting both infrastructure failures and sophisticated cyber-intrusions. While machine-learning methods dominate recent literature, this paper revisits purely mathematical algorithmic approaches for real-time intrusion detection inside Red Hat–based cloud VMs. We present a lightweight detection engine that fuses statistical change-point analysis with matrix based anomaly scoring, achieving 97.1% accuracy on the CICIDS2017 dataset while consuming 62% less CPU than an LSTM baseline.The engine is packaged as an Ansible playbook for seamless integration into Red Hat Enterprise Linux (RHEL) + KVM stacks, and its performance is evaluated under multi-region active–active replication powered by Ceph storage. Experimental results demonstrate sub-second detection latency (<800
ms) and negligible memory overhead (<18MB per VM), confirming that mathematically rigorous, AI-free solutions remain viable for cost sensitive or regulated environments.