Exploring Open Access Cybersecurity Datasets for Machine Learning-Based Cyberattack Detection

In recent years, machine learning has transitioned from the confines of the laboratory to real-life applications. Such advancement has brought significant improvements in various domains, including cybersecurity. Machine learning techniques are now being leveraged to enhance malware detection, identify breaches, and notify industries about possible security risks. With the growing prevalence of cyberattacks, network security has emerged as a critical concern. The objective of this paper is to provide an overview of major cybersecurity datasets, their properties, and their suitability for different machine learning methods. This study specifically focuses on the datasets utilized in machine learning and artificial intelligence approaches, as they serve as essential tools for analyzing network traffic and detecting anomalies. By examining diverse cybersecurity datasets, this research aims to shed light on their characteristics, strengths, and limitations. Additionally, it explores their compatibility with various machine learning techniques, facilitating informed decisions regarding dataset selection for network traffic analysis. The findings presented in this paper will help researchers and practitioners in the field of cybersecurity understand the available datasets and their applicability in developing effective machine learning models for network security.