ABSTRACT
Zero-day vulnerabilities are one of the critical challenges in modern cybersecurity as these attack software flaws that organizations do not have enough time to patch, before being exploited. This work investigates advanced detection and mitigation techniques against the increased threats from zero-day vulnerability. This paper tests the efficiency of collaborative threat intelligence frameworks, the usage of automated patch management systems and hybrid detection methodologies in combination with modern achievements. Prominent real-world applications show that preventive security methods are in high demand, besides the extent of implications as demonstrated in the case of the Log4j vulnerability and the HAFNIUM incident. The results are oriented toward behavior-based systems, machine learning capabilities, and ZTA toward enhancing protection offered by cybersecurity. Additionally, the methodology developed also comprises real-time surveillance capabilities through artificial intelligence, quick incident response mechanisms, and even vulnerability anticipation to diminish risk. This paper provides a holistic framework for organizations wishing to bridge and reduce the ever-growing complexity of zero-day threats.
