Application of time series algorithms in cybersecurity

This paper explores the application of time series algorithms to enhance anomaly detection in cybersecurity. Windows log files such as PowerShell Operational, Windows Defender, Firewall, System, and others were analyzed, focusing on those with the highest informational potential and data volume. Various models were used: exponential smoothing (Holt-Winters), Prophet, Fourier analysis, and Kalman filter for modeling seasonal, periodic, and linear patterns in system events. Advanced methods include LSTM and GRU neural networks, as well as ensemble algorithms like Random Forest and XGBoost, which demonstrated high accuracy in detecting unusual behavior. Special emphasis was placed on dynamic models, such as Bayesian Structural Time Series, to understand system states over time. Experiments show that applying multiple models enables a robust and adaptive approach to log analysis, especially for early detection of attacks and deviations from norms. The proposed framework highlights the importance of predictive analytics in preventive cybersecurity and provides a foundation for developing intelligent systems for real-time monitoring and response.