In the constantly evolving field of information management systems, ensuring appropriate security measures to prevent cyber invasions is of paramount significance. The dynamic and complex nature of contemporary cyberthreats, especially in the context of the Internet of Things (IoT), frequently proves too much for traditional intrusion detection systems (IDS).  The current study emphasises on the difficulties of achieving high precision and real-time speed while maintaining data confidentiality. This study presents an new structure that combines Quantum Feedforward Neural Networks (QFNNs) with Contextual Rule-based Signature Detection (CRSD) to enhance IoT security. QFNNs leverage the principles of quantum computation to proficiently handle high-dimensional IoT network data, resulting in important improvements in detection speed and accuracy. Meanwhile, the Contextual Signature Detection module dynamically adjusts detection processes based on contextual parameters, such as device behavior, network traffic patterns, and temporal fluctuations, ensuring flexible and precise threat identification. The proposed QFNNs were assessed utilizing IoT intrusion datasets and established greater presentation related to conventional neural networks and standard signature-based methods. The findings indicate notable developments in detection accuracy, a decrease in false positives, and developed adaptability to evolving threats. By integrating the computational advantages of quantum neural networks with the adaptability of contextual rule-based detection, this method proposals a scalable and resilient solution for safeguarding IoT networks