Reducing False Positives in API Security: Advanced Techniques Using Machine Learning."*

 The article discusses advanced techniques for reducing false positives in API security through machine learning. It emphasizes the importance of training models on diverse datasets to improve accuracy and context understanding. Techniques such as anomaly detection, supervised learning, and the incorporation of feedback loops are highlighted. The article also addresses the significance of continuous monitoring and adaptation of models to evolving threats. Overall, the approach aims to enhance API security while minimizing the operational impact of false alerts.

https://securityboulevard.com/2024/10/reducing-false-positives-in-api-security-advanced-techniques-using-machine-learning/

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