<![CDATA[Intrusion detection plays an important role in protecting systems from various threats. However, as intrusion techniques become more sophisticated, traditional detection methods have shown limitations in identifying new attacks. This research addresses the pressing issue of improving intrusion detection by utilizing Convolutional Neural Networks (CNN) algorithms, compared to various other machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Trees, and Gradient Boosting (GBoost). The main objective is to evaluate and compare the performance of these algorithms using a comprehensive dataset sourced from Kaggle, which includes 25,192 data and 42 features. Using metrics such as accuracy, precision, recall, and F1-score, the results show a complex pattern in the strengths and weaknesses of each. Surprisingly, CNN achieved exceptional accuracy, raising questions that require further investigation. Notably, KNN stands out as the best-performing machine learning algorithm. Contextualized within existing research, this study advances the understanding of the role of machine learning in intrusion detection, providing valuable insights for practical implementation. The findings reinforce the relevance of adapting to the evolving network threat landscape while raising interesting questions for future research. In conclusion, this research provides a comparative analysis of intrusion detection techniques, offering a basis for making informed decisions to improve network security and mitigate evolving threats.]]>
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