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Network intrusion detection systems (NIDSs) is one of the primary components in any network security infrastructure. Currently, many NIDSs that are developed are rule-based systems and their performances mainly depend on the rule sets. But rules formation becomes a tedious and rime consuming task due to the enormous amount of network traffic. This is overcome by using data mining techniques. These techniques build intrusion detection models adaptively. Random forests is an efficient data mining algorithm which can be used for network intrusion detection This can be used for real time intrusion detection. In this paper, we discuss the approaches for feature selection, and optimization of parameters of random forests, compare different models, and also discuss the other method for detecting the anomalies across active networks.