Cybersecurity Data Science: Toward Advanced Analytics, Knowledge, and Rule Discovery for Explainable AI Modeling

In a computing context, cybersecurity technology and operations are constantly changing, and data science is driving the change. Building a data-driven model that extracts patterns in cybersecurity incidents is the key to automating and intelligently managing a security system. This chapter mainly explores the convergence of cybersecurity and data science exploring its transformative potential in fortifying digital defenses. Throughout the chapter, advanced analytics, knowledge, and rule discovery as well as corresponding data-driven framework are highlighted within the broader area of cybersecurity data science. An emphasis is given to the pivotal role of explainable modeling in comprehending and mitigating sophisticated cyber threats as the threat landscape evolves. Thus the role of knowledge and rule discovery is explored briefly advocating for a paradigm shift toward explainable modeling to address the evolving nature of today’s diverse cyber threats. Data-driven insights and knowledge discovery are explored through methodologies, tools, and best practices, providing a roadmap for practitioners and researchers. Overall, this chapter describes data-driven real-world applications in the context of cybersecurity that not only empower organizations to be proactive in their cyber defense but also highlight the need for transparency and explainable modeling.