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The attack named Distributed Denial of Service (DDoS) that takes place in the large blockchain network requires an efficient and robust attack detection and prevention mechanism for authenticated access. Blockchain is a distributed network in which the attacker tries to hack the network by utilizing all the resources with the application of enormous requests. Several methods like Rival Technique, filter modular approach and so on, were developed to detect and prevent the DDoS attack in the blockchain; still, detection accuracy is a challenging task. Hence, this research introduces an efficient technique using optimization-based deep learning by considering the blockchain network and smart contract for the detection and prevention of DDoS attacks.Based on the user request, the traffic is analyzed, and the verification using the smart contract is made to find the authenticated user. After the verification, the response is provided for the authenticated user, and the suspicious traffic is utilized for the detection of DDoS attacks using the Poaching Raptor Optimization-based deep neural network (Poaching Raptor-based DNN), in which the classifier is tuned using the proposed optimization algorithm to reduce the training loss. The proposed algorithm is designed by hybridizing the habitual practice of the raptor by considering the concurring behavior, hunting style along with poaching behavior of the Lobo to enhance the detection accuracy. After the attack detection, the nonattacker is responded, and the attacker is prevented by entering the IP/MAC address in the logfile. The performance of the proposed method is evaluated in terms of recall, precision, FPR, and accuracy and obtained the values of 96.3%, 98.22%, 3.33%, and 95.12%, respectively.

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The growing popularity of Software Defined Networks (SDN) and the Internet of Things (IoT) has led to the emergence of Software Defined Internet of Things (SDIoT) based on centralized network management by the Control Plane, which can handle the dynamic nature of IoT devices and the high volume of network traffic. However, due to their specific design, SDIoTs are the ideal target for Distributed Denial of Service (DDoS) attacks, becoming one of the most destructive threats. Machine learning (ML) techniques are best suited to solve this problem due to the recent growth and sophistication of DDoS attacks. In this study, we propose an enhanced deep learning approach based on combining AutoEncoder (AE) and Extreme Gradient Boosting (XGBoost). First, we applied the SHapley Additive exPlanations (SHAP) feature selection method to select the appropriate features subset according to their correlation results. Next, the AE is trained on the previous subset to learn a compact representation of the input features. The latent representation generated by the AE is then used as input for the XGBoost model, which is trained to predict the target variable and classify the traffic as usual or attack. In parallel, Grid Search Cross Validation (GSCV) is used to find the optimal hyperparameters for the AE‐XGBoost. The experimental results using two publicly available realistic SDN‐Iot datasets demonstrate that the proposed approach enables precise identification of DDoS attacks in SDIoT networks, achieving a 99.9920% accuracy, an F1 score of 0.999917, and a low false positive rate. Furthermore, the proposed model's performance exceeds that of the models used for comparison.

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