Real-Time Network Intrusion Detection System Using Deep Learning: Identifying Cyber Attacks Through Anomaly Pattern Recognition

Abstract

Modern network infrastructure faces increasingly sophisticated cyber attacks, from distributed denial-of-service (DDoS) to zero-day exploits and advanced persistent threats (APTs). Traditional signature-based intrusion detection systems (IDS) struggle to detect novel attacks that do not match known patterns, while rule-based approaches generate high false-positive rates and require constant manual tuning. At the same time, network traffic volume and complexity have grown exponentially, demanding real-time detection capabilities that can process millions of packets per second without introducing latency. This paper presents a Real-Time Network Intrusion Detection System (RT-NIDS) that leverages deep learning to identify cyber attacks through anomaly pattern recognition. The framework integrates a hybrid architecture combining Convolutional Neural Networks (CNN) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal sequence modeling, and autoencoders for unsupervised anomaly detection. A lightweight attention mechanism focuses computational resources on the most discriminative traffic features, enabling real-time inference on commodity hardware. The system was evaluated on three benchmark datasets (CICIDS2017, UNSW-NB15, and CSE-CIC-IDS2018) containing 11 major attack categories, achieving 98.7% detection accuracy, 97.8% precision, and 97.6% recall with an average detection latency of 1.2 milliseconds per flow. Critically, the system demonstrates strong zero-day generalization: when trained on CICIDS2017 and tested on previously unseen UNSW-NB15 attack variants, cross-dataset accuracy remains above 94.2%, substantially outperforming traditional machine learning classifiers (82.3%) and signature-based systems (68.1%). False positive rates are reduced by 64% compared to rule-based IDS through context-aware anomaly scoring. Deployment in a live enterprise network (3,200 endpoints, 45 Gbps peak traffic) over six months validated real-world feasibility, detecting 127 actual intrusion attempts with zero false negatives among critical threats and only 3.2 false alarms per day. These results demonstrate that deep learning-based NIDS can provide enterprise-grade security with practical detection speed, adaptability to evolving threats, and operational manageability.