Large-scale IoT malware analysis and classification method

Recently, Internet of things (IoT) malware emerges in large numbers and attacks IoT devices in cyberspace. However, the family characteristics of IoT malwares are not obvious due to the open-source problem, a more fine-grained malware classification method is needed to solve the problems of advanced threat malware discovery and attack organization tracking. To address this question, we took a large-scale analysis of 157 911 IoT malwares which have been found from May 2019 to May 2020, and labeled a dataset which includes 9 categories and 12 278 malwares. Then we proposed an IoT malware classification method whose main idea is extracting complex structure features including FCG graph and text by static reverse analysis. The learning features using graph representation learning and text representation learning were used, and the experiments on the labeled dataset show that the average recall rate is 88.1%. Our method has been taken into practice and works well. © 2022, Editorial Board of JBUAA. All right reserved.