A Comparative Study of Watering Hole Attack Detection Using Supervised Neural Network (2311.15024v3)

Abstract: The state of security demands innovative solutions to defend against targeted attacks due to the growing sophistication of cyber threats. This study explores the nefarious tactic known as "watering hole attacks using supervised neural networks to detect and prevent these attacks. The neural network identifies patterns in website behavior and network traffic associated with such attacks. Testing on a dataset of confirmed attacks shows a 99% detection rate with a mere 0.1% false positive rate, demonstrating the model's effectiveness. In terms of prevention, the model successfully stops 95% of attacks, providing robust user protection. The study also suggests mitigation strategies, including web filtering solutions, user education, and security controls. Overall, this research presents a promising solution for countering watering hole attacks, offering strong detection, prevention, and mitigation strategies.