Smart cities have received greater attention over the recent years. Despite its popularity, unsecured smart city networks have become potential back door entry points. Distributed Denial of Service (DDoS) attacks are one of the most widespread threats to smart city infrastructure. In this paper, a smart city intrusion detection framework based on Restricted Boltzmann Machines (RBMs) is proposed. RBMs are applied due to their ability to learn high-level features from raw data in an unsupervised way and handle real data representation generated from smart meters and sensors. On top of these extracted features, different classifiers are trained. The performance of the proposed methodology is tested and benchmarked using a dataset from a smart water distribution plant. The results show the efficiency of the proposed methodology in attack detection with high accuracy. In addition, the proposed methodology outperforms the classification model applied without features learning step.