Video anomaly detection is the process of localizing abnormal video patterns spatiotemporally. The extraction of spatiotemporal features incorporating both local and global spatiotemporal dependency from the video is one of the essential and challenging tasks for video anomaly detection. In order to solve this issue, this article proposes a novel deep reconstruction model-based Self-Attention-enabled Spatio-Temporal Network (SASTNet) model that uses Self-Attention-enabled Convolutional Bidirectional Long-Short-Term-Memory-based Auto-Encoder (SAConvBiLSTMAE) architecture. A novel Self-Attention-enabled Convolutional Bidirectional Long-Short-Term-Memory (SAConvBiLSTM) block is designed to learn the global spatiotemporal dependencies during the frame reconstruction in an end-to-end autoencoder framework for detecting video anomalies. The foundation of the proposed SASTNet model is supported by a comprehensive mathematical problem formulation using a deep reconstruction approach for detecting video anomalies in a single-scene scenario. The selection of an appropriate threshold is one of the crucial factors in video anomaly detection problems. Hence, an efficient strategy has been implemented to select the optimal threshold by utilizing the Receiver Operating Characteristics (ROC) curve for detecting video anomalies. Finally, extensive experimental analysis and comparison with state-of-the-art approaches using three bench-marked single-scene video anomaly datasets, such as UCSD Ped1, UCSD Ped2, and CUHK Avenue, indicate the superiority of the proposed framework in video anomaly detection.