Overfitting and Security Risks

Overfitting occurs when a machine learning model learns noise and details from training data too well, resulting in poor generalization to new data. This can lead to significant security risks, as overfitted models may become vulnerable to adversarial attacks and data manipulation. Understanding and mitigating overfitting is crucial in developing robust AI systems that maintain integrity and security. Implementing techniques like cross-validation and regularization can help ensure models perform well in real-world scenarios, safeguarding sensitive information and enhancing overall cybersecurity.

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