Continuous and unending growth of data created so many challenges in data mining task. Data mining is extraction meaningful information i. e. knowledge from large datasets for the future decision making. The data which is continuously generating with changing values is known as streaming data. We face many problems with streaming data as we are unable to store it and process it. Network data is one of the best examples of streaming data. Intrusion Detection System (IDS) used to detect the malicious user to protect the network. System's safety in a network is a prime important factor. In this paper, we present comprehensive approach to improve performance of IDS by applying some classification techniques with streaming dataset. For the experiment purpose we created our own network dataset which shows significant accuracy in results after applying classifiers.

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Data Mining Naive Bayes Hoeffding Tree Intrusion Detection System (ids)