The present invention relates to the field of networking and API/application security. In particular, the invention is directed towards methods, systems and computer program products for deep learning based API traffic analysis and network security. The invention provides an automated approach to threat and/or attack detection by machine learning based accumulation and/or interpretation of various API/application traffic patterns, identifying and mapping characteristics of normal traffic for each API, and thereafter identifying any deviations from the normal traffic parameter baselines, which deviations may be classified as anomalies or attacks.