Network security is a growing concern as digital infrastructure expands, and traditional measures struggle against modern cyber threats. With the increasing complexity of attacks, there is a need for more adaptive and intelligent solutions. This research introduces AI2DS (Advanced Deep Autoencoder-Driven Method for Intelligent Network Intrusion Detection Systems), an autoencoder-based architecture that enhances security by identifying deviations from normal network behavior. The model is trained on normal data, using reconstruction errors to detect anomalies through adaptive thresholding. By simplifying attack classification into a single ‘intrusion’ class, AI2DS demonstrates high accuracy and broad applicability. The model shows average improvements of $\mathbf{6. 6 1 \%}, \mathbf{3 1. 1 1 \%}, \mathbf{1 1. 4 6 \%}$, and $\mathbf{1 6. 9 1 \%}$ in Precision, Recall, F1 Score, and Accuracy, respectively, over state-of-the-art methods, with potential for real-time application and future advancements.