Enhancing Security and Trust in IoT Networks with Blockchain-Enabled OGANN for Malicious Node Detection and Trust Management
DOI:
https://doi.org/10.63282/3050-922X.ICAILLMBA-103Keywords:
Iot Security, Blockchain, Generalized Additive Neural Networks, Malicious Node Detection, Trust Management, Identity ManagementAbstract
The Internet of Things (IoT) connects a large number of devices to exchange data and support different applications. As the number of connected devices increases, security problems such as fake nodes, malicious behavior, and lack of trust among devices are becoming more common. These issues mainly arise because IoT networks are distributed in nature and do not rely on a centralized authority. To address these challenges, this paper presents a security framework that uses Blockchain technology along with an Optimized General Additive Neural Network (OGANN). In this approach, information related to a node, such as its identity details and behavior during communication is taken as input. When a new IoT node tries to enter the network, its identity is checked through smart contracts on the Blockchain which helps in stopping unauthorized access. After joining the network, the behavior of the node is continuously monitored and given to the OGANN model to identify malicious activities. Based on the model output, the node is classified and a trust value is assigned accordingly. All important events such as authentication results, trust updates, and detection alerts are stored on the Blockchain so that the records cannot be altered. Nodes whose trust value goes below a fixed threshold are considered suspicious and may be isolated from the network. Simulation results show that the proposed method improves security and helps in maintaining trust among IoT devices while reducing incorrect detection.
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