Zero-Day Exploit Detection: Analyzing Machine Learning Approaches for Detecting Zero-Day Exploits and Previously Unseen Vulnerabilities to Enhance Proactive Threat Defense
Keywords:
Zero-day exploits, Threat detectionAbstract
Zero-day exploits pose a significant threat to cybersecurity by exploiting vulnerabilities that are unknown to the software vendor and, therefore, lack a patch. Detecting these exploits before they can be weaponized is critical for proactive threat defense. This paper reviews machine learning approaches for zero-day exploit detection, focusing on their effectiveness, efficiency, and applicability. Various algorithms and techniques are discussed, highlighting their strengths and limitations. The paper also explores the challenges and future directions in this field to enhance cybersecurity defense mechanisms.
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