Abstract: Cyber threat hunting is a proactive cybersecurity approach focused on identifying threats that evade traditional security measures. It involves the integration of human expertise, data analytics, and advanced tools to detect anomalies within organizational networks and systems. Despite its potential, many organizations remain dissatisfied with their threat hunting programs due to gaps in required analytical skills and the lack of integration of advanced techniques such as machine learning. This paper explores the design of an effective threat hunting exercise, examining its role in complementing traditional security measures. It emphasizes the importance of advanced data analytics, threat intelligence integration, and automation to enhance the effectiveness of threat hunting. The proposed framework underscores the significance of the data collection and analysis process, improving detection rates and reducing the impact of advanced threats. This study also addresses the challenges faced in threat hunting, including skills gaps and the need for better tools, and outlines strategies for overcoming these obstacles to create more robust security programs.

Keywords: Threat hunting, Frameworks, Security

Pages: 142-148