 This paper proposes a machine learning-based approach to detect HTTP attacks using the TensorFlow library. It collects and transforms network traffic data into vectors, which are then used to train a machine learning model. The model achieves high accuracy on the tested dataset, demonstrating its effectiveness in identifying HTTP attacks with minimal false positives. This article was authored by Martin Chavinik, Martin Hason, Martin Haverilla, and others.