From Data to Defense: How Ontology Fuels AI in Cyber Threat Detection

In today's evolving digital landscape, cybersecurity threats [1] have become increasingly complex and persistent, with attacks like data breaches and ransomware exploiting vulnerabilities in digital systems. As organizations handle growing amounts of data, robust defense strategies depend on comprehensive datasets for detecting and preventing threats. This paper addresses data scarcity in cybersecurity by proposing the development of a dataset ontology tailored to the domain. In data science, ontologies are structured frameworks that define relationships between different concepts within a specific field. A dataset ontology for cybersecurity serves as a cohesive framework for categorizing, organizing, and interconnecting datasets, making it easier for professionals to access and analyze threat-relevant data. The objective is to fill the gap in existing datasets and enable more precise, data-driven cybersecurity strategies. Every cyber-attack generates data, such as network logs, malware metadata, or phishing records. Professionals use this data to analyze threats, understand attack methods, and devise preventive strategies.