With the rapid development of science and technology, information security issues have been attracting more attention. According to statistics, tens of millions of computers around the world are infected by malicious software (Malware) every year, causing losses of up to several USD billion. Malware uses various methods to invade computer systems, including viruses, worms, Trojan horses, and others and exploit network vulnerabilities for intrusion. Most intrusion detection approaches employ behavioral analysis techniques to analyze malware threats with packet collection and filtering, feature engineering, and attribute comparison. These approaches are difficult to differentiate malicious traffic from legitimate traffic. Malware detection and classification are conducted with deep learning and graph neural networks (GNNs) to learn the characteristics of malware. In this study, a GNN-based model is proposed for malware detection and classification on a renewable energy management platform. It uses GNN to analyze malware with Cuckoo Sandbox malware records for malware detection and classification. To evaluate the effectiveness of the GNN-based model, the CIC-AndMal2017 dataset is used to examine its accuracy, precision, recall, and ROC curve. Experimental results show that the GNN-based model can reach better results.