AI TO PREDICT ZERO DAY VULNERABILITIES

AI TO PREDICT ZERO DAY VULNERABILITIES

Mr. Swarnil Sambhaji Shinde
Mr. Siddhesh Jaywant Shivalkar

Computer Engineering – Universal College of Engineering

ABSTRACT

Artificial intelligence (AI) holds significant potential in predicting zero-day vulnerabilities—undisclosed software flaws that attackers exploit before they are identified or addressed by developers. These vulnerabilities pose a critical challenge to cybersecurity, as their detection often relies on reactive measures following an attack. By analyzing historical vulnerability data, AI and machine learning techniques can identify patterns and characteristics indicative of software components prone to zero-day vulnerabilities.

This predictive approach enables the early identification of high-risk areas within software systems, providing an opportunity to address potential threats proactively. AI-driven solutions can enhance traditional cybersecurity measures by shifting the focus from reactive detection to anticipatory defense. Such advancements in predictive capabilities not only reduce the risk of exploitation but also strengthen the overall resilience of digital ecosystems in an increasingly complex threat landscape.

Keywords: Zero-Day Vulnerabilities, Artificial Intelligence, Machine Learning, Predictive Analysis, Cybersecurity, Threat Detection, Proactive Defense