Leveraging AI for Zero-Day Attack Detection
Challenges and Future Directions
Keywords:
zero-day attacks, artificial intelligence, cybersecurity, machine learningAbstract
Zero-day attacks pose a significant threat to cybersecurity, exploiting vulnerabilities that are unknown to software vendors and security professionals. These attacks can lead to severe financial and reputational damage to organizations, making early detection critical. Artificial intelligence (AI) offers promising solutions for identifying these threats through advanced pattern recognition and anomaly detection techniques. This paper examines the application of AI models in detecting zero-day attacks, highlighting the unique challenges faced in their early detection. It further explores future research directions aimed at enhancing detection accuracy, including the integration of machine learning techniques, improved data gathering methods, and the development of more robust algorithms. The findings underscore the potential of AI to transform zero-day attack detection, but also emphasize the need for ongoing research to address existing limitations.
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