AI-Driven Predictive Analytics for Reducing False Positives in Cyber Threat Detection
DOI:
https://doi.org/10.63345/cq9g9m24Keywords:
Cyber Threat Detection, False Positives, Predictive Analytics, Machine Learning, Deep Learning, AI in CybersecurityAbstract
Cybersecurity systems often struggle with a high number of false positives, leading to alert fatigue and inefficiencies in threat detection. AI-driven predictive analytics offers a solution by leveraging machine learning and deep learning models to distinguish between legitimate threats and false alarms. This paper explores the application of AI in reducing false positives in cyber threat
detection. It reviews existing methods, discusses the challenges associated with traditional rulebased systems, and proposes an AI-driven framework that enhances accuracy. Experimental results demonstrate that integrating AI reduces false positives by over 40%, significantly improving cybersecurity response efficiency.
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Articles are published under the Creative Commons Attribution NonCommercial 4.0 License (CC BY NC 4.0), allowing others to distribute, remix, adapt, and build upon the work for non-commercial purposes while crediting the original author.
