In the current days with the growth of communication systems, the Internet of Things (IoT) has become a famous mechanism that allows large systems to be allowed with connectivity with heterogeneous frameworks. Nevertheless, it exists with technical complexity in the existing networks to manage certain massive systems in an effective way. Nowadays, the Software Defined Network (SDN) method with its elasticity and agility has been integrated with IoT to face the powerful flexibility and scale demands and create a novel IoT framework. Effective routing models with high security and low latency are needed, as the SDN-IoT architecture’s size is enhanced. However, the existing SDN routing models are still suspicious of flow control’s dynamic change, more importantly when the network is under threat. The IoT systems are normally performed in unattended and hostile environments. In addition, the routing in the present IoT framework becomes ineffective because of the existence of unauthenticated and malicious nodes, insecure routing, minimum network lifespan, and so on. In order to manage these problems, this work designs an effective SDN routing strategy-enabled IoT system with a blockchain mechanism to prevent malicious threats during data transmission. The deep learning strategy is supportive for recognizing suspicious IoT devices based on each node’s energy features. This article performs two significant tasks including the identification of malicious nodes and the selection of the optimal path. At first, the data of the IoT node is stored in the blockchain since the nodes in the IoT have a constrained lifetime. In addition, the nodes are validated to verify the authentication by applying a smart contract. The Cascaded Dilated Recurrent Neural Network (CD-RNN) is employed for recognizing the malicious and trusted nodes of the network. After recognizing the malicious node, the selection of the optimal route is carried out. In this, the routes are chosen optimally by the Transitive Phase of Pelican Optimization (TPPO). Lastly, the estimation is conducted by considering some factors including security, Packet Delivery Ratio (PDR), delay, and throughput. Hence, the suggested system offers better functionality than the previous approaches. The suggested scheme presents a hybrid mechanism, which combines a CD-RNN-based malicious node detection with TPPO-based routing optimization and blockchain-based trust management that guarantee a high level of security and performance in SDN-IoT settings.
In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain technology, and various U-Net based architectures. Genetic algorithms are used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of blockchain technology. Finally, an ensemble learning system based on voting mechanism was developed to combine local outputs of various segmentation models into a global output. Our method shows that strong performance in a condensed number of epochs may be achieved with a high learning rate and a small batch size. As a result, we are able to perform better than standard solutions for well-known medical databases. In fact, the proposed solution reaches 95% of intersection over the union, compared to the baseline solutions where they are below 80%. Moreover, with the proposed blockchain strategy, the detected attacks reached 76%.
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