AI For Cybersecurity: Real-Time Anomaly Detection in Network Traffic Using AI

Abstract

The large-scale, high-speed characteristics of network traffic in the modern digital world and the constantly changing threat environment requires real-time detection of anomalies in cybersecurity infrastructure. The paper is a synthesis and review of recent studies (2015-2024) at the crossroads of artificial intelligence (AI) and network traffic anomaly detection, with a specific focus on real-time or near real-time detection. The review features classical statistical techniques, ML-based techniques, DL, and hybrid/edge paradigm that allow rapid detection. Then identify key limitations in current frameworks (such as latency, false-alarm rates, model drift, adversarial robustness, and feature- engineering overhead) and propose a conceptual architecture that extends prior work by integrating lightweight unsupervised online learning, edge computing, and adversarial regularization for real-time responsiveness and scalability. The analysis wraps up with unresolved issues and research directions in the future of AI-based anomaly detection of network traffic.