<![CDATA[Anomaly Detection in Network Security Systems Using Machine Learning highlights the importance of developing effective models for data security. This research aims to develop an adaptive and automated anomaly detection model using the Naive Bayes algorithm and cross-validation. The methodology applied includes security log data collection, data preprocessing, implementation of Naive Bayes algorithms, and model evaluation using metrics such as accuracy, precision, recall, and F1-score. The results showed that the developed model was able to achieve high accuracy in detecting anomalies, with significant performance in identifying real threats without negative errors. The implication of this research is the improvement of network security through the application of machine learning, providing practical solutions for practitioners to deal with increasingly complex cybersecurity challenges]]>
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