AI-Based Early Detection of Cyber Attacks using Network Traffic Analysis – A Comprehensive Review

Cyberattacks have dramatically increased in recent years, exposing the flaws in traditional intrusion detection systems (IDS) that rely on static signatures or flimsy anomaly models. Conventional techniques have a high false positive rate and struggle to detect novel, polymorphic, or multi-stage attacks. Artificial intelligence (AI) has developed into a powerful enabler of data-driven and adaptive network defines in order to get around these restrictions. ML, DL, RL, and GNNs are used by AI-powered IDS to automatically extract features from data, analyse complex traffic patterns, and identify minute irregularities that could indicate malicious activity. This review compiles research from 2023 to 2025 that critically assesses hybrid ensembles, supervised and unsupervised frameworks, and novel concepts like explainable and federated learning. Comparative experiments show that AI-based IDS perform with greater accuracy and flexibility on various datasets, but issues persist around dataset imbalance, scalability, adversarial robustness, and interpretability. Industrial applications are examined in enterprise networks, cloud systems, Internet of Things (IoT) environments, and real-time monitoring. Future directions are also pointed out that can influence resilient, trustworthy, and scalable IDS for future cybersecurity