<p>The rise of more interconnected systems and digital exchanges has escalated the occurrence and intricacy of various kinds of cyber threats. In this regard, it is imperative to ensure smart, effective detection to ensure the security and reliability of networks. However, existing methods for intrusion detection are found to be deficient in terms of precision and generalization. Feature representation tends to be clunky, and security information filtering from traffic logs, users, and other sources poses another level of challenge. From this perspective, there is a clear need to develop an automated, precise, fast-adapting intrusion detection system that can assess threats and respond in real time. This article proposes an automated model for intrusion detection that consists of several state-of-the-art components. These include Stochastic Triangular Fuzzy Number Normalization (STFNN) for data normalization. Additionally, it includes a Deep Complex Convolutional Transformer Network (DCCTN) for extracting relevant features. Finally, there is a Quantum Dual-Domain Convolutional Neural Network (QDDCNN) trained using the Farmer Ants Optimization Algorithm (FAOA). The efficacy of QDDCN-FAOA can be ascertained by comparing its performance with existing models, as indicated by performance tests conducted on two popular benchmarks, NSL-KDD and UNSW-NB15, which reveal a better accuracy rate, predicted to be as high as 99.43%, in comparison to NSL-KDD, and 99.71%, as opposed to UNSW-NB15, coupled with a higher precision, recall, and F1 score, and with a lower error value, as indicated by RMSE and MAE, reflecting its robustness and reliability for real-time scenarios. In general, such a framework presents a good, intelligent idea of an intelligent system of automated intrusion detection and evaluation of threats, with a high degree of accuracy and flexibility in security analytics.</p>