ENHANCING DATA SECURITY REWARD MODEL REPUTATION IN BLOCK CHAIN USING ARTIFICIAL NEURAL NETWORK - Ayuluri Sri Lakshmi; Prasad Devarasetty; M James Stephen; Anumula Sruthi (Utilitas Mathematica, 2025)
This paper introduces a novel block chain-based approach for secure and efficient database management. Block chain technology, with its decentralized, immutable, and transparent nature, offers significant advantages over traditional systems, particularly in enhancing data security, integrity, and auditability. It takes large number of devices to collectively train a global model by collaborating with a server datasets on their respective premises. We design a decentralized attribute-based access control mechanism with an auxiliary Trust and Reputation System (TRS) for IoT authorization We present the research paper is developed practical privacy security analytics in information systems.With that note, this paper proposes a new model called Block Chain based Advanced Data Security-Reward Model (DSecCS) for enhancing data security and attack resistance. There has been a significant rise in the volume of information produced as well as the take different involved in its data type. We propose a mapping framework to employ a fine-tuned multilayer feed forward artificial neural network (ANN) and extreme learning machine (ELM) for role engineering in the SCADA-enabled IIoT environment to ensure privacy and user access rights to resources. The proposed model comprises of three sections, as, Construction of Intellectual CS Model, Confusion Model and Incorporation of Block-Chain.Support Vector Machine (SVM), k-nearest Neighbors (KNN), and Convolutional Neural Network (CNN) in terms of accuracy, false positive rate, false negative rate, precision, recall.