In the dynamic landscape of cybersecurity, safeguarding computer networks against persistent malicious threats is paramount. Intrusion Detection Systems are crucial in this context by monitoring network traffic for unau-thorized access. While the integration of Machine Learning and Deep Learning has significantly advanced intrusion detection, the persistent challenge lies in effectively detecting minority attack classes. This study introduces an innovative approach that combines SHapley Additive exPlanations(SHAP) for feature selection and Deep Neural Networks(DNN) to enhance the performance of intrusion detection systems, particularly focusing on minority attack classes in the NSL-KDD dataset. Applied to a Random Forest classifier using a balanced dataset, SHAP provides valuable insights into feature importance, refining the feature set for seamless integration into a DNN architecture. Employing the NSL-KDD dataset, the research concentrates on elevating the detection accuracy for User-to-Root attack and Root-to-Local attacks. The results showcase a notable improvement in performance along with a reduction in computational time compared to using all the available features. A key emphasis of the study is on detecting all attack types without compromising the F1-score. An in-depth analysis of the initial set of 41 features identifies 30 as crucial for effective intrusion detection. On the imbalanced dataset, SHAP-based feature reduction improved the overall F1-score in multiclass classification from 86% to 91% by reducing training time by 8.86%, confirming that SHAP can lower complexity without sacrificing accuracy. However, several minority attacks remained undetected due to their extremely low representation. Additional experiments with oversampled data confirm that SHAP continues to provide efficiency gains while enabling robust detection of rare attack classes. These findings demonstrate that SHAP-based feature selection improves efficiency in IDS and has strong potential for minority attack detection if data scarcity is addressed. This research not only contributes to the enhancement of IDS capabilities but also highlights the importance of meticulous feature selection in achieving comprehensive and efficient intrusion detection.