Stochastic Analysis of Cyber Threats in Information Systems

This research delves into stochastic methods for density estimation, focusing on techniques such as kernel density estimation (KDE), jittering, and handling multimodality. It aims to leverage these methods to detect cyber threats based on the density distributions of system queries. KDE provides a nonparametric approach to estimating the probability density function of a random variable, allowing for flexible modeling of complex data patterns. Jittering introduces controlled randomness into data points, enhancing the robustness of density estimation against overfitting and improving generalization performance. Moreover, addressing multimodality is crucial for accurately capturing diverse behaviors within the query distributions, which is essential for cyber threat detection in heterogeneous environments. By combining these stochastic methods, this study seeks to enhance the reliability and efficiency of cyber threat detection systems by providing more nuanced insights into the underlying data distributions, thus enabling proactive measures against emerging threats.