Detecting anomalies in dynamic networks is essential for a range of real-world applications, such as social networks and cybersecurity. However, it encounters substantial challenges due to the diverse and ever-evolving nature of these anomalies. We present Attention-based Anomaly Detection In Dynamic Network (AADDN), an innovative end-to-end anomaly detection framework that utilizes attention mechanisms to capture the complex interactions between node attributes (individual characteristics) and structural features (collective patterns) across various time stamps. Unlike traditional methods that depend on heuristic rules with limited scope, AADDN employs a dual autoencoder architecture to learn comprehensive representations in the latent space, allowing the model to more effectively identify both individual and collective anomalies. By emphasizing the integrated learning of temporal, structural, and attribute information, our approach surpasses existing methods, showcasing superior anomaly detection capabilities in dynamic network environments.