Network intrusion detection systems (NIDSs) are fundamental components in the protection of computer networks. Yet, apprehensions arise concerning the viability and endurance of existing methodologies in meeting the challenges posed by contemporary networks. In particular, these concerns are linked to the escalating need for human involvement and the diminishing accuracy of detection. This chapter evaluates existing methodologies and proposes an approach to develop an efficient IDS using principal component analysis (PCA) with random forest algorithm. The dataset used for this study is NSL-KDD, the benchmark for modern-day internet traffic. However, just like any other network traffic dataset, the NSL-KDD also suffers from a class imbalance problem. To overcome this, SMOTE is used to oversample minority attack classes. The results are then compared to show that the proposed method works more efficiently than existing machine learning approaches. The proposed approach has an accuracy of 96.78% and an error rate of 0.21%.