Cybersecurity Analytics

This book provides essential knowledge for cybersecurity and data science practitioners, focusing on practical applications in cybersecurity analytics.

This book serves as a comprehensive resource, consolidating essential mathematics, probability, statistics, and machine learning knowledge needed for professionals engaged in cybersecurity analytics. Cybersecurity Analytics is tailored for both cybersecurity students and professionals eager to master data science techniques that are pivotal in addressing various cybersecurity challenges. It also appeals to data science practitioners who wish to understand the specific adaptations required for the cybersecurity field.

Readers interested in practical applications, such as creating malware or phishing email detectors, will find Cybersecurity Analytics particularly beneficial. The book emphasizes a hands-on learning approach, providing numerous examples and links to datasets that allow readers to engage in real-world applications. With a foundational understanding of college-level calculus and basic probability, most of the material is accessible, making it suitable for a wide audience.

The content is structured into chapters that cover a range of topics, including unsupervised learning, semi-supervised learning, supervised learning, text mining, and natural language processing. Additionally, it offers foundational knowledge in security, statistics, and linear algebra. To further support serious practitioners, the accompanying website features a collection of datasets, updates, and other valuable resources, enhancing the learning experience.