• Deepak Kumar
• Sanjay Kumar Pal

Anomaly detection is a critical task in various domains, including cybersecurity, healthcare, finance, and industrial systems, where identifying deviations from normal behavior can prevent significant losses and enhance decision-making processes. The advent of Artificial Intelligence (AI) has revolutionized anomaly detection, offering sophisticated methods that can automatically learn complex patterns and detect anomalies with high accuracy. This paper presents a comprehensive review of AI techniques for anomaly detection, covering both traditional methods and modern approaches, such as machine learning and deep learning. The review examines various algorithms, including supervised, unsupervised, and semi-supervised learning techniques, and explores their applications across different fields.

"Artificial Intelligence Techniques for Anomaly Detection: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.c185-c187, November-2024, Available :http://www.jetir.org/papers/JETIR2411224.pdf

"Artificial Intelligence Techniques for Anomaly Detection: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppc185-c187, November-2024, Available at : http://www.jetir.org/papers/JETIR2411224.pdf