In order to safeguard their critical systems against network intrusions, organisations deploys multiple Network Intrusion Detection System (NIDS) to detect malicious packets embedded in network traffic based on anomaly and misuse detection approaches. The existing NIDS deal with a huge amount of data that contains null values, incomplete information, and irrelevant features that affect the detection rate of the IDS, consumes high amount of system resources, and slowdown the training and testing process of the IDS. In this paper, a new feature selection model is proposed based on hybrid feature selection techniques (information gain, correlation, chi squere and gain ratio) and Principal Component Analysis (PCA) for feature reduction. This study employed data mining and machine learning techniques on NSL KDD dataset in order to explore significant features in detecting network intrusions. The experimental results showed that the proposed model improves the detection rates and also speed up the detection process.