Article’s

The rapid increase in cyberattacks across modern digital infrastructures has highlighted the urgent need for intelligent and adaptive security solutions. Traditional intrusion detection systems (IDS), though widely deployed, struggle to detect novel, evasive, and complex threats due to their dependence on predefined signatures and static rules. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a powerful alternative for intrusion detection by learning from historical attack patterns and modeling anomalous behaviors. This research paper provides a structured overview of AI-driven intrusion detection techniques, focusing on signature-based, anomaly-based, and hybrid ML models. The paper examines their mechanisms, performance strengths, limitations, and practical applicability across different network environments. Furthermore, it evaluates commonly used datasets such as NSL-KDD and CICIDS2017 that form the foundation of IDS research. The study also identifies challenges in deploying ML-based IDS, including interpretability issues, scalability constraints, adversarial vulnerabilities, and false positive rates.