EDGNN-Forensics: An Explainable Dynamic Graph Neural Network Framework for Robust IoT Cybercrime Detection Using CICIoT2023 and NF-ToN-IoT Datasets
Abstract
The fast expansion of Internet of Things (IoT) infrastructures has made cybercrime more sophisticated, making traditional intrusion detection methods ineffective for detecting coordinated and changing attacks. Standard static feature learning or independent network-flow analysis methods offer poor explainability for forensic inquiry. This paper proposes EDGNN-Forensics, an Explainable Dynamic Graph Neural Network framework that models IoT communications as dynamic graphs and integrates graph representation learning, hybrid graph-based anomaly detection, explainable graph intelligence, and adaptive cybercrime decision optimisation into a single end-to-end architecture to address these limitations The approach uses dynamic communication graphs to preserve temporal and structural relationships and graph attention and hybrid anomaly learning to identify known and unknown assaults. Node, edge, and analyst trust estimation are provided via an Explainable Graph Intelligence Layer for interpretable forensic analysis. Experimental evaluation using CICIoT2023 and NF-ToN-IoT benchmark datasets. The suggested model had 99.61% accuracy, 99.50% F1-score, and 0.999 AUC on the CICIoT2023 dataset and 99.42% accuracy, 99.27% F1-score, and 0.999 AUC on the NF-ToN-IoT dataset. This shows that EDGNN-Forensics outperforms graph-based and hybrid intrusion detection methods for accurate, explainable, and scalable cybercrime detection in current IoT systems
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