Comparison of Machine Learning Algorithm in Intrusion Detection Systems: A Review Using Binary Logistic Regression

In the era of increasing cyber threats, the implementation of robust Intrusion Detection Systems (IDS) is crucial for safeguarding network integrity. This study presents a comprehensive comparison of various machine learning algorithms employed in IDS, with a specific focus on binary logistic regression as a comparative tool. We utilized a well-established dataset to evaluate the performance of multiple algorithms, including decision trees, support vector machines, and neural networks. Our findings reveal significant variations in accuracy, precision, and recall across the different algorithms. Binary logistic regression served as an effective benchmark, highlighting the strengths and weaknesses of each model. This research contributes to the ongoing discourse in cybersecurity by providing empirical evidence on the efficacy of machine learning approaches in detecting intrusions, offering insights for future enhancements in IDS design.