AUTOMATED DETECTION OF ANOMALIES IN CORPORATE WIRELESS NETWORK TRAFFIC USING PYTHON: METHODS, IMPLEMENTATION, AND EFFECTIVENESS EVALUATION

This article presents the results of a study focused on the development and comparative evaluation of models for automated anomaly detection in corporate wireless network traffic. The introduction substantiates the relevance of cybersecurity challenges in the context of increasing Wi-Fi traffic volumes and the growing complexity of attack types, which necessitate the use of intelligent intrusion detection systems. The theoretical foundations section reviews signature-based and behavioral analysis concepts, IDS/WIDS system principles, and modern approaches to anomaly detection using machine learning and deep learning. Special attention is given to explainable artificial intelligence (XAI) and its role in enhancing model transparency. The data selection and preprocessing section describes the use of two representative datasets — AWID-3 and UNSW-NB15 — covering a wide range of attacks and normal traffic. Preprocessing steps included data cleaning, normalization, categorization, and class balancing using SMOTE and random undersampling. The implementation section outlines the architectures of SVM, Random Forest, XGBoost, and CNN-GRU models, using Scikit-learn, TensorFlow, Keras, and SH