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The security landscape necessitates creative solutions to defend againsttargeted attacks due to the growing sophistication of cyber threats. This studyexplores the nefarious tactic known as "watering hole attacks using supervisedneural networks to detect and prevent these attacks. The neural networkidentifies patterns in website behavior and network traffic associated withsuch attacks. Testing on a dataset of confirmed attacks shows a 99% detectionrate with a mere 0.1% false positive rate, demonstrating the model'seffectiveness. In terms of prevention, the model successfully stops 95% ofattacks, providing robust user protection. The study also suggests mitigationstrategies, including web filtering solutions, user education, and securitycontrols. Overall, this research presents a promising solution for counteringwatering hole attacks, offering strong detection, prevention, and mitigationstrategies.

A Comperative Study of Watering Hole Attack Detection Using Supervised Neural Network