AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection DOI
Mutaz Abdel Wahed

Gamification and Augmented Reality., Journal Year: 2025, Volume and Issue: 3, P. 112 - 112

Published: April 13, 2025

Introduction: zero-day attacks pose a critical cybersecurity challenge by targeting vulnerabilities that are undisclosed to software vendors and security experts. Conventional threat intelligence approaches, which rely on known signatures attack patterns, often fail detect these stealthy threats.Methods: this study proposes comprehensive framework combines AI technologies, including machine learning algorithms, natural language processing (NLP), anomaly detection, analyze threats in real time. The incorporates predictive modeling anticipate potential vectors automated response mechanisms enable rapid mitigation.Results: the findings indicate AI-enhanced significantly improves detection of compared traditional methods. reduces time enhances accuracy identifying subtle anomalies indicative exploits.Conclusion: research highlights transformative strengthening against attacks. By leveraging advanced real-time analytics, proposed offers more robust adaptive approach cybersecurity.

Language: Английский

AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection DOI
Mutaz Abdel Wahed

Gamification and Augmented Reality., Journal Year: 2025, Volume and Issue: 3, P. 112 - 112

Published: April 13, 2025

Introduction: zero-day attacks pose a critical cybersecurity challenge by targeting vulnerabilities that are undisclosed to software vendors and security experts. Conventional threat intelligence approaches, which rely on known signatures attack patterns, often fail detect these stealthy threats.Methods: this study proposes comprehensive framework combines AI technologies, including machine learning algorithms, natural language processing (NLP), anomaly detection, analyze threats in real time. The incorporates predictive modeling anticipate potential vectors automated response mechanisms enable rapid mitigation.Results: the findings indicate AI-enhanced significantly improves detection of compared traditional methods. reduces time enhances accuracy identifying subtle anomalies indicative exploits.Conclusion: research highlights transformative strengthening against attacks. By leveraging advanced real-time analytics, proposed offers more robust adaptive approach cybersecurity.

Language: Английский

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