Optimization of Malware Detection and Defense Algorithm Based on Hybrid Convolutional Graph Neural Network

ABSTRACT With the development of information technology, malicious software attack methods are becoming increasingly sophisticated, posing a serious threat to network security. Aiming at the problems of existing malware detection technologies, such as low detection rate, high false alarm rate, and poor real‐time performance, this paper proposes a malware detection and defense algorithm based on a hybrid convolutional graph neural network. This algorithm combines the advantages of CNN and GNN to effectively identify malicious behavior by extracting deep‐seated malware features. The experimental results show our algorithm has high accuracy and real‐time performance in malware detection. We employed publicly available malware datasets during the experiment for training and testing. After comparative experiments, the detection rate reaches 95.2%, which is 8.6% higher than the CNN algorithm and 5.3% higher than that of the GNN algorithm. In terms of false alarm rate, the algorithm in this paper is only 1.2%, far lower than the traditional CNN algorithm's 5.8% and the GNN algorithm's 3.5%. At the same time, our algorithm also shows excellent performance in real‐time, and the average detection time is only 0.12 s, which is 20% lower than the traditional algorithm. In addition, to verify the defense capability of the algorithm, we simulate various malware attack scenarios in our experiments. The results show that our algorithm can effectively identify and defend against 90.6% of malware attacks, which strongly supports network security protection.