A Lightweight and Explainable Machine Learning Approach for Intrusion Detection

In the digital era, the detection of malicious network activities and an unauthorized and still remains a critical challenge. Intrusion Detection Systems (IDS) is a vital player in safeguarding digital infrastructures; Due to the advancement in technologies cyber-attacks necessitates the adoption of intelligent, data-driven approaches. This work explores the application of logistic regression, a statistical machine learning technique, for intrusion detection using the NSL-KDD dataset. The research emphasizes the model's efficiency, interpretability, and capability to differentiate normal and malicious traffic in binary classification tasks. Experimental results shows that the logistic regression achieves a detection accuracy of 91.6%, with strong precision and recall metrics, making it suitable for resource-constrained or real-time environments. Comparative analysis with alternative classifiers highlights logistic regression's computational advantages and transparency. The research reveals that, despite its simplicity, logistic regression presents a robust and explainable solution to mitigate intrusion detections and offers a foundation for further enhancements through hybrid and adaptive modeling techniques .