The rapid expansion of IoT networks is leaving them susceptible to threats like DDoS attacks, which have the potential to affect the operation of vital services. This work proposes a hybrid intrusion detection system combining GPUaccelerated CuDNNLSTM and CNNLSTM models to capture both spatial and temporal traffic features. Using the Kitsune dataset with nine attack scenarios, the models were trained and tested in a Kaggle GPU environment (NVIDIA Tesla T4/P100, CUDA 11.x, CUDNN 8.x). The hybrid approach achieved over 98% accuracy, 97% precision, and ROCAUC above 0.98, outperforming classical ML baselines such as SVM and Random Forest. SHAP explanations provided transparency by highlighting key features behind each detection, while blockchain logging ensured tamperproof records of attack events. This system grants its users a clear view of the process logic by breaking it down into SHAP-based Explainable AI, which demonstrates decisive features for each decision. The attacks identified are stored in a safe manner on the Ethereum blockchain via smart contracts, making the solution difficult to tamper with using any intrusion technique. Therefore, the proposed approach presents a very viable option for reliable intrusion detection in an efficient and faster version for designing a DDoS detection system with new network configurations. Challenges include blockchain latency and deploying resource-constrained IoT devices. Future work will explore lightweight variants and federated learning to improve scalability.