Özet:

Abstract In this work, we propose a novel known as matrix translation and Elliptic curve cryptography (MT-ECC) approach for secured data communication in IoT network systems. The proposed approach includes phases such as the key generation stage, encryption stage; cluster-based secure routing stage, and decryption stage. Moreover, we introduced two types of tables such as string location relying on ASCII value and Prime number generation table and space reference table. Meanwhile, after the completion of key generation on the transmitter side, the data is transmitted to the receiver side and authorized users can access the data without any loss. Besides, if any attacks happen means it is necessary to detect the intrusion and normal data, and for that purpose we propose a novel Deep Neural Network (DNN) based Gazella optimization (GO) algorithm which effectively detects the intrusion and separates the normal data traffic from the available datasets. For the experimental purpose, we have taken the Kaggle dataset and implemented it in MATLAB and the comparative results show that the proposed approach is effectively used for secured communication and detects intrusion efficiently in case attacks happen.

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