The main goal of intrusion detection system (IDS) is to monitor the network performance and to investigate any signs of any abnormalities over the network. Recently, intrusion detection systems employ machine learning techniques, due to the fact that machine learning techniques proved to have the ability of learning and adapting in addition to allowing a prompt response. This work proposes a model for intrusion detection and classification using machine learning techniques. The model first acquires the data set and transforms it in the proper format, then performs feature selection to pick out a subset of attributes that worth being considered. After that, the refined data set was processed by the Konstanz information miner (KNIME). To gain better performance and a decent comparative analysis, three different classifiers were applied. The anticipated classifiers have been executed and assessed utilizing the KNIME analytics platform using (CICIDS2017) datasets. The experimental results showed an accuracy rate ranging between (98.6) as the highest obtained while the average was (90.59%), which was satisfying compared to other approaches. The gained statistics of this research inspires the researchers of this field to use machine learning in cyber security and data analysis and build intrusion detection systems with higher accuracy.