An Applied Framework for Real-Time Network Intrusion Detection Using Optimized Ensemble Learning in Enterprise IT Environments.

Abstract

Modern enterprise IT environments require proactive, real-time intrusion detection systems to combat increasingly sophisticated and high-speed cyber threats. However, existing machine learning and deep learning models face a critical trade-off between predictive accuracy and inference latency, rendering many computationally heavy frameworks unsuitable for line-rate, high-throughput network streams. To address this practical deployment gap, this study proposes a novel, lightweight applied framework for real-time network intrusion detection using an optimized ensemble learning architecture. The proposed methodology utilizes a stacking ensemble strategy that combines highly efficient gradient boosting base learners with a meta-classifier. To minimize computational overhead without sacrificing sensitivity, a Bayesian Optimization algorithm is implemented to dynamically tune the multidimensional hyperparameters. Evaluated against the comprehensive CSE-CIC-IDS2018 dataset, empirical results demonstrate that the proposed framework achieves an outstanding Detection Rate of 99.85% and a minimal False Positive Rate of 0.04%. Crucially, the optimized architecture maintains a sub-millisecond inference latency of 0.92 milliseconds per flow, significantly outperforming traditional Convolutional Neural Networks (CNN) which recorded severe latencies of 4.20 milliseconds alongside lower detection accuracy. Ultimately, this research delivers a deployable, highly accurate architectural solution that successfully overcomes the latency constraints of conventional models, making it highly viable for real-time enterprise security operations.