*Result*: A study on watering hole attack recognition.

*In light of the increased sophistication of cyber threats, the current state of security necessitates creative ways to guard against targeted attacks. There are two primary methodologies being used in research in order to identify effects of watering hole attacks in the organizations. It has been observed that potential need of defend against this attacks. As a result, understand the attack pattern is the necessary approach. This study investigates the malevolent watering hole attack technique and employs supervised neural networks, towards identifying the pattern in addition to propose a strategical prevention approach. Behavior of websites and network traffic that signal the occurrence of such attacks are recognized by neural network. study concentrates on a dataset comprising proven attacks demonstrates proposed model accuracy based on detection rate of 99% with a false positive rate of 0.1%. The recommended approach offers strong user security through preventive measures, successfully blocking 95% of potential threats. In addition of inspecting the performance of proposed model study recommends mitigating techniques, that incorporates filtering data from the determined nodes it's also important to education programs for the users, and take necessary measures for security controls. [ABSTRACT FROM AUTHOR]*