Visible to the public Detecting Malware Using Graph Embedding and DNN

TitleDetecting Malware Using Graph Embedding and DNN
Publication TypeConference Paper
Year of Publication2022
AuthorsWang, Rui, Zheng, Jun, Shi, Zhiwei, Tan, Yu'an
Conference Name2022 International Conference on Blockchain Technology and Information Security (ICBCTIS)
KeywordsData models, data structures, Deep Learning, Deep Neural Network, feature extraction, feature vector, graph embedding, graph theory, Human Behavior, Information security, Malware, malware analysis, malware detection, Markov processes, Metrics, privacy, pubcrawl, resilience, Resiliency, Resiliency Coordinator
AbstractNowadays, the popularity of intelligent terminals makes malwares more and more serious. Among the many features of application, the call graph can accurately express the behavior of the application. The rapid development of graph neural network in recent years provides a new solution for the malicious analysis of application using call graphs as features. However, there are still problems such as low accuracy. This paper established a large-scale data set containing more than 40,000 samples and selected the class call graph, which was extracted from the application, as the feature and used the graph embedding combined with the deep neural network to detect the malware. The experimental results show that the accuracy of the detection model proposed in this paper is 97.7%; the precision is 96.6%; the recall is 96.8%; the F1-score is 96.4%, which is better than the existing detection model based on Markov chain and graph embedding detection model.
DOI10.1109/ICBCTIS55569.2022.00018
Citation Keywang_detecting_2022