Paper Key : IRJ************487
Author: Sivananda Reddy Julakanti
Date Published: 07 Mar 2025
Abstract
Zero-day vulnerabilities are one of the most dangerous forms of cyber threats. A zero-day vulnerability refers to a flaw in software that is unknown to the software vendor or developer, leaving it exposed to malicious attacks. Such vulnerabilities are often exploited by hackers before they are detected or patched. AI has proven to be a promising tool in detecting and mitigating various cyber threats, including zero-day vulnerabilities. By leveraging machine learning (ML) and other AI-driven methods, organizations can predict, identify, and neutralize these threats effectively. This paper discusses the role of AI in mitigating zero-day vulnerability threats, alongside various other threats like phishing, insider threats, session hijacking, spyware, ransomware, XSS, and denial of service attacks. The research will explore the methods of detection, classification, and mitigation strategies enabled by AI, especially focusing on machine learning algorithms, deep learning, and anomaly detection. Furthermore, the paper will provide examples with code implementations to demonstrate AI's effectiveness in real-world scenarios and propose the future scope of AI integration in cybersecurity. The challenges and limitations of current AI models will also be discussed to provide a balanced view of its role in cybersecurity.
DOI LINK : 10.56726/IRJMETS68483 https://www.doi.org/10.56726/IRJMETS68483