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Network intrusion detection is one of the central systems used in cyber security to prevent the intrusions in the organisation's networks. Tackling the attempts to compromise the confidentiality, integrity and availability of computer networks' security mechanisms in a big data environment is the most challenging task due to the volume and variety of big data. This study presented to tackle the challenges in network Intrusion Detection Systems (IDS) and demonstrate intelligent algorithms' development to detect the intrusions in big network data. The problem is the practical selection of the features from the network dataset as it dramatically impacts the intrusion detection accuracy. Hence, an efficient feature selection approach must be introduced to achieve higher accuracy with a reduced number of features.