Data Sources for Machine Learning Models in Cybersecurity

Abstract

In the ever-evolving field of cybersecurity, machine learning (ML) models have emerged
as powerful tools for detecting, analyzing, and mitigating various cyber threats. The
effectiveness of these models is fundamentally dependent on the quality and diversity of
the data sources they utilize. This paper provides an in-depth exploration of the different
types of data sources essential for building robust ML models in cybersecurity. It
examines structured and unstructured data, such as network traffic logs, endpoint data,
system logs, threat intelligence feeds, and dark web data, and discusses their unique
contributions to enhancing cyber threat detection and response capabilities. The paper
also presents case studies and examples that highlight the practical application of these
data sources in real-world scenarios, illustrating how they improve model accuracy,
adaptability, and resilience against emerging threats. Additionally, it addresses the
challenges associated with data collection, storage, privacy, and quality, and proposes
best practices for optimizing data usage in ML models. Finally, the paper outlines future
directions for research and development, emphasizing the integration of novel data
sources and advanced data analytics techniques to further strengthen ML-driven
cybersecurity solutions.