The proliferation of Internet of Things (IoT) devices has precipitated a substantial expansion in network size and data volume, concurrently giving rise to heightened security concerns. Consequently, there has been a concerted effort in research to augment the execution and prediction performance of Network Intrusion Detection Systems (NIDS). The efficacy of machine learning (ML) models crucially hinges on judicious choices in algorithms and feature sets. While much of the previous research on feature selection algorithms concentrated on static datasets, real-world network intrusion detection systems grapple with the challenges posed by streaming data. This study comprehensively reviews feature selection algorithms, shedding light on their merits, drawbacks, and practical applications, with a specific emphasis on prerequisites for effective processing of streaming data. Noteworthy contributions include bridging gaps in prior research by delineating requirements tailored to the challenges of streaming data and conducting experimental analyses using contemporary datasets.