Enhancing Cyber Security : A Study of Data Preprocessing Techniques for Cyber Security Datasets

Abstract

In today's fast-changing digital world, cybersecurity is a critical concern because of heightened frequency and sophistication of cyber threats. As a result, the need for effective data preprocessing techniques has become increasingly essential for processing and analyzing cybersecurity datasets in order to identify and mitigate potential risks. The study begins by outlining the unique characteristics of cybersecurity datasets, including their high dimensionality, imbalanced class distribution, and presence of noise and outliers. Subsequently, it examines a range of preprocessing techniques such as data cleaning, transformation, normalization, and feature selection, highlighting their applicability and effectiveness in the context of cybersecurity. It gives systematic analysis of different preprocessing detection, feature selection, and normalization. (Brightwood & Seraphina Brightwood, 2024) By implementing appropriate data preprocessing techniques, cybersecurity professionals can enhance the accuracy and effectiveness of their predictive models, intrusion detection systems, and other cybersecurity methods such as data cleaning, outlier solutions.