HUMAN IN THE LOOP CYBER DEFENSE SYSTEM

This paper presented a Human in the Loop Cyber Defense System that integrates machine learning-based threat detection with human expert validation to enhance cybersecurity effectiveness. The proposed framework addresses the limitations of both fully automated and manual systems by combining the speed and scalability of machine intelligence with the contextual reasoning and judgment of human analysts. By introducing a structured human validation layer and a feedback-driven learning mechanism, the system significantly reduces false positives and improves detection accuracy. The adaptive nature of the framework enables continuous improvement based on analyst feedback, making it suitable for dynamic and evolving cyber threat environments. Experimental analysis demonstrates that the proposed approach achieves better precision, reliability, and trustworthiness compared to traditional automated systems. The system is particularly effective in critical domains such as financial systems, healthcare networks, and enterprise security operations where accurate decision-making is essential. Overall, the Human in the Loop Cyber Defense System provides a scalable, intelligent, and practical solution for modern cybersecurity challenges, paving the way for more reliable and adaptive defense mechanisms in future digital ecosystem.

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