We present a real-world application that uses a quantum computer. Specifically, we trained a Restricted Boltzmann Machine (RBM) using quantum annealing (QA) to develop an intrusion detection system. RBMs were trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs were also trained using contrastive divergence (CD) which is a classical method. D-Wave's 2000Q quantum annealer has been used to implement QA. Our analysis of the ISCX data shows that the dataset is imbalanced and we present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the oversampling of attack instances. The imbalanced training dataset was divided into five sub-datasets that were trained separately. A majority voting was performed to get the final result. Our results show the majority vote increased the classification accuracy up from 90.24% to 95.68% in the case of CD. For the case of QA, the classification accuracy increased from 74.14% to 80.04%. In the second scheme, an RBM was used to generate synthetic data to balance the training dataset. The RBMs trained on synthetic data generated from a CD-trained RBM performed comparably to the RBMs trained on synthetic data generated from a QA-trained RBM. Balanced training data was used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) performed better than other classifiers. They both showed an accuracy of 93%. Our results show a proof of concept that a QA-based RBM can be trained on a binary dataset, with 64-bit records. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques.