With the rapid growth of network traffic, malicious users are rapidly developing new methods of network intrusion. To face these threats, current security systems have to develop high accuracy, short response time, and a never seen agility in recognizing the before-never-seen threats. One of the most crucial components of security systems is the Intrusion Detection System (IDS). This paper explores Machine Learning (ML) approaches for IDS. In this article we propose an IDS based on ensemble voting. We perform testing on real-world data using the UNSW-NB15 dataset and employing an unbalanced database with four different classification algorithms: Decision Tree, Random Forest, K-nearest neighbor, and Multiple Layer Perceptron. The voting ensemble classification method is used to improve the accuracy of the model and reduce the number of false positives. By using Deep Learning (DL) we also increase the possibility of discovery of new attacks. This research has also the goal of increasing the explainability of anomaly-based Network IDS, a problem now central in the literature of ML and DL-based systems.