This study presents an investigation of adversarial attacks on classical machine learning algorithms within the context of network threat detection. It offers an overview of the machine learning models employed for various tasks in the realm of computer network security. A formal description of the threat model is provided, along with a classification of adversarial attacks. The classification of network traffic within the WEB-IDS23 dataset is carried out using classical machine learning models, including k-nearest neighbors, random forest, and support vector machine. Implemented adversarial attacks include the Fast Gradient Sign Method, Projected Gradient Descent, the Carlini and Wagner attack, and DeepFool, applied to these machine learning algorithms. An analysis of the impact of the deployed adversarial attacks on the aforementioned classical machine learning algorithms is conducted.