@inproceedings{1bfde138512f4c388da087fb33d5efd3,
title = "Detecting Malware Using Graph Embedding and DNN",
abstract = "Nowadays, 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.",
keywords = "deep neural network, feature vector, graph embedding, malware detection",
author = "Rui Wang and Jun Zheng and Zhiwei Shi and Yu'an Tan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022 ; Conference date: 15-07-2022 Through 17-07-2022",
year = "2022",
doi = "10.1109/ICBCTIS55569.2022.00018",
language = "English",
series = "Proceedings - 2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "28--31",
booktitle = "Proceedings - 2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022",
address = "United States",
}