Optimizing cyberattack frequency forecasting by advanced machine learning adaptation with tumbling windows.

Significant research has been directed towards cyberattack detection through reactive assistive techniques, utilizing pattern-matching algorithms for scanning system logs and network traffic to identify known signatures. While effective machine learning (ML) models have been developed to automate detection processes-including identifying, tracking, and blocking malware and intruders-comparatively less attention has been given to cyberattack prediction, particularly for time scales extending beyond daily observations. Long-term attack prediction approaches are highly valued, offering defenders extended periods for the development and dissemination of defensive strategies and tools. Long short-term memory (LSTM) networks are frequently chosen among ML models for their strong performance in time series data analysis. This paper presents a novel methodology combining LSTM models with flexible sliding window techniques to improve prediction outcomes and enhance training efficiency. Experiments also incorporate convolutional filters and combined bivariate performance measures, similar to methodologies used in enhancing stock forecasting systems. These substantial contributions are anticipated to offer valuable insights for researchers in this field.