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The geometric increase in the usage of computer networking activities poses problems with the management of network normal operations. These issues had drawn the attention of network security researchers to introduce different kinds of intrusion detection systems (IDS) which monitor data flow in a network for unwanted and illicit operations. The violation of security policies with nefarious motive is what is known as intrusion. The IDS therefore examine traffic passing through networked systems checking for nefarious operations and threats, which then sends warnings if any of these malicious activities are detected. There are 2 types of detection of malicious activities, misuse detection, in this case the information about the passing network traffic is gathered, analyzed, which is then compared with the stored predefined signatures. The other type of detection is the Anomaly detection which is detecting all network activities that deviates from regular user operations. Several researchers have done various works on IDS in which they employed different machine learning (ML), evaluating their work on various datasets. In this paper, an efficient IDS is built using Ensemble machine learning algorithms which is evaluated on CIC-IDS2017, an updated dataset that contains most recent attacks. The results obtained show a great increase in the rate of detection, increase in accuracy as well as reduction in the false positive rates (FPR).