Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research.