<p>The security of IoT is growing sophistication of cyberattacks that makes system in Software-Defined Network (SDN) is crucial. Using the NSL-KDD dataset, this research introduces a Bi-GRU framework combined with an Entropy-Augmented Multi-attention Mechanism (MAC) for intrusion detection. As a way to detect dynamic and evolving threats, the Bi-GRU model makes use of the ability to detect time-dependent and sequential patterns in network traffic. By identifying critical anomalies, the entropy-based augmentation conduct improves the detection capability while also making it accessible to the model to successfully concentrate on opposed behaviors. The multi-attention approach further enhances priority, make sure that the most essential network traffic non features are used for classification. The Proposed structure beats current methods in field testing with an amazing accuracy of 99.57 percentage. It is quite appropriate for complicated and dynamic IoT-SDN structure due to its durability, scalability, and dependability. The efficiency of the BI-GRU framework is demonstrated by a comparative comparison with other states of art methods which consistently outperform of accuracy, precision, recall, and F1 score. In addition to enhancing intrusion detection performance, this unique combination of Bi-GRU and entropy-augmented multi-attention mechanisms solves the challenges of identifying a variety of attack patterns in modern IoT systems. The Proposed approach presents a highly accurate, efficient, and scalable method for boosting IoT security in SDN infrastructures, representing a substantial advancement in intrusion detection systems.</p>