Comprehensive Evaluation of Machine Learning Algorithms for Intrusion Detection: A Focus on Binary Logistic Regression

Intrusion Detection Systems (IDS) are crucial in safeguarding network infrastructures against unauthorized access and malicious activities. With the increasing complexity and volume of cyber threats, traditional signature-based detection methods are often inadequate. Consequently, there has been a significant shift toward utilizing machine learning algorithms to enhance the effectiveness of IDS. This study presents a comprehensive evaluation of various machine learning algorithms applied to intrusion detection, with a particular focus on Binary Logistic Regression (BLR).We begin by reviewing the current landscape of intrusion detection techniques, highlighting the distinct advantages of machine learning over conventional methods. This review encompasses a selection of established algorithms, including Decision Trees, Support Vector Machines, Random Forests, and Neural Networks, positioning BLR as a benchmark for comparison. The methodology involves a rigorous selection of datasets, including KDD Cup 1999 and CICIDS, ensuring a robust analysis of performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.Through systematic experimentation, we assess the performance of each algorithm under controlled conditions, utilizing data preprocessing techniques and cross-validation methods to ensure reliability. The results reveal that while Binary Logistic Regression demonstrates competitive performance, particularly in terms of interpretability and computational efficiency, other algorithms such as Random Forests and Neural Networks may offer superior accuracy in complex scenarios.The discussion section delves into the implications of these findings for IDS design, emphasizing the importance of feature selection and algorithmic transparency. This study not only contributes to the existing body of knowledge by providing a comparative framework for evaluating machine learning algorithms in intrusion detection but also offers practical recommendations for deploying these models in real-world applications. Future research directions are proposed to further explore the integration of ensemble methods and the impact of adversarial attacks on IDS performance.