Cybersecurity is undergoing new types of sophisticated, unknown and evolving attacks, which current signature-based intrusion detection solutions cannot par parenthetically detect. These systems, while effective against known threats, suffer from major flaws: The problem with many new malware detection approaches is that they cannot detect zero-day attacks, are sensitive to attack variants and generate false positives. To address these challenges, machine learning based approaches have been considered. In this study, real-time anomaly detection is conceived to be enhanced by artificial intelligence particularly Recurrent Neural Networks (RNN) and attention mechanism. The above-mentioned techniques enable more precise, context-sensitive detection of threats or the evaluation of temporal sequences of network data. Thus the inclusion of attention mechanisms reduces the number of false positives and increases the accuracy of the systems while also making the system more flexible for dynamic environments. The main focus of this investigation is to create an intrusion detection solution which unites an attention mechanism with RNN. The proposed method strives to detect advanced and unidentified cyber-attacks by both recognizing temporal relationships in network activities and selecting the most important security features.