Comparative Study of Supervised Learning Algorithms for Intrusion Detection with a Focus on Logistic Regression

Intrusion Detection Systems (IDS) play a critical role in safeguarding networks from malicious activities and unauthorized access. The rapid evolution of cyber threats necessitates the adoption of advanced methodologies, particularly in the realm of machine learning, to enhance detection capabilities. This study presents a comprehensive comparative analysis of various supervised learning algorithms employed in IDS, with a particular focus on Binary Logistic Regression. The research begins with a detailed exploration of the fundamental principles of IDS and the challenges associated with traditional detection methods. It subsequently examines the role of machine learning in addressing these challenges, highlighting the advantages of supervised learning techniques. A selection of algorithms, including Decision Trees, Support Vector Machines (SVM), Random Forests, and Neural Networks, is evaluated alongside Logistic Regression to determine their effectiveness in real-world scenarios. Utilizing benchmark datasets such as the KDD Cup and UNSW-NB15, this study employs a rigorous methodology that encompasses data preprocessing, model training, and hyperparameter tuning. Performance is assessed using key metrics—accuracy, precision, recall, and F1 score—facilitating a robust comparison across algorithms. The findings reveal significant insights into the performance dynamics of Logistic Regression in the context of IDS, demonstrating its strengths in interpretability and efficiency while also identifying limitations in handling complex data patterns. Comparative results indicate that while Logistic Regression offers advantages in specific scenarios, other algorithms may outperform it in terms of overall detection accuracy and resilience against sophisticated attacks. This research contributes to the existing body of knowledge by providing a nuanced understanding of the applicability of supervised learning algorithms in intrusion detection, with implications for practitioners aiming to optimize IDS performance. Future work is suggested to explore hybrid models that integrate the strengths of multiple algorithms, enhancing the robustness and adaptability of IDS in an ever-evolving threat landscape.