<![CDATA[This article discusses the importance of information system security in the current technological era and how the increasingly complex threat of cyber attacks demands a more sophisticated approach to detection and prevention. This initial study explores the potential of applying Machine Learning in cyber intrusion detection as a first step to developing detection systems that are adaptive and responsive to evolving threats. Through a methodology involving the collection of representative data on cyber attacks, data preparation, and Machine Learning model selection, this article describes the initial stages for understanding and testing the potential of this technology in the context of cyber security. Although it includes an example dataset, data preparation steps, and the selection of several Machine Learning algorithms, this study only gets to the model selection stage, while the model training process and performance evaluation are the focus of future work. The conclusions of this initial study emphasize the importance of selecting appropriate algorithms with specific features for effective intrusion detection against growing cyber threats.]]>
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