<![CDATA[The rapid growth of information technology and digital activities has led to an increasing number of attacks on computer networks. Various types of cyberattacks, such as unauthorized access and malicious activities, pose serious threats to data security and confidentiality. Therefore, an effective security mechanism is required to detect such attacks, one of which is through the implementation of an Intrusion Detection System (IDS). This study aims to apply the Naïve Bayes algorithm to detect unauthorized access in computer networks. The dataset used in this research is the UNSW-NB15 dataset, which was preprocessed to obtain 82,332 records suitable for classification. The evaluation was conducted using the 10-Fold Cross Validation method with the assistance of RapidMiner software. The experimental results indicate that the Naïve Bayes algorithm achieves excellent performance in classifying network traffic. The proposed model attained an accuracy of 95.96%, a precision of 94.11%, a recall of 98.85%, and an Area Under the Curve (AUC) value of 0.964. The high AUC value demonstrates that the model is highly effective in distinguishing between normal traffic and attack traffic. Based on these findings, it can be concluded that the Naïve Bayes algorithm is a reliable and effective method for intrusion detection systems to enhance network security.]]>
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