Optimized Multi‐Channel Graph Neural Network Framework for Intrusion Detection in Internet‐of‐Things Environments

ABSTRACT

The Internet of Things (IoT) and its applications occupy a prominent place in contemporary research. The IoT, with its inherently heterogeneous behavior offers solutions to numerous problems and has become inseparable from human life. In this paper, an Optimized Multi‐Channel Graph Neural Network Framework for Intrusion Detection in Internet‐of‐Things Environments (DGSEMGNN‐IDF‐IoT) is proposed. Here, the data collected from the Telemetry of Network‐Internet of Things (ToN‐IoT) dataset are used. To execute this, the input data are given into the pre‐processing stage using the Dual Central Difference Kalman Filter (DCDKF) to eliminate redundant values and replace missing ones. The pre‐processed data are supplied to the feature selection stage using the Ebola Optimization Search Algorithm (EOSA) to select the optimum features. The selected features are fed into the Dynamic Global Structure Enhanced Multi‐Channel Graph Neural Network (DGSEMGNN) to effectively categorize the data as normal, backdoor, scanning, password, Cross‐Site Scripting (XSS), ransomware, injection, Denial of Service (DOS), Distributed Denial of Service (DDOS) and Man‐in‐the‐Middle (MITM) attack. Then, the Wolf‐Bird Optimizer (WBO) is used to enhance the weight parameters of DGSEMGNN. The DGSEMGNN‐IDF‐IoT is implemented, and the performance metrics, such as accuracy, f1‐score, specificity, sensitivity, Receiver Operating Characteristic (ROC), error rate, and computation time, are analyzed. The DGSEMGNN‐IDF‐IoT approach attains 6.80%, 10.41%, and 6.60% higher accuracy, 6.95%, 8.02%, and 10.9% higher f1‐score when compared with existing methods.