Unveiling Drift in Machine Learning Models Integrating with Cybersecurity

Abstract

Machine Learning models are often trained on historical data with the assumption that the future data will follow similar pattern. However, in real world applications data shifts over time due to factors such as change in market conditions and user behaviour, resulting in drift. Drift leads to the degradation of model performance, making its management critical for reliable predictions in dynamic environments. This study offers a thorough examination of types of drift and existing detection techniques, including statistical and machine learning based approach. Furthermore, the research will examine the significance drift in cybersecurity, where adaptive strategies introduce both data and concept drift. The aim of this paper is to bridge the gap between machine learning theories with practical security applications.