{"title":"基于API调用的深度学习鲁棒恶意软件检测系统","authors":"Yingying Liu, Yiwei Wang","doi":"10.1109/ITNEC.2019.8728992","DOIUrl":null,"url":null,"abstract":"With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.","PeriodicalId":202966,"journal":{"name":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"A Robust Malware Detection System Using Deep Learning on API Calls\",\"authors\":\"Yingying Liu, Yiwei Wang\",\"doi\":\"10.1109/ITNEC.2019.8728992\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.\",\"PeriodicalId\":202966,\"journal\":{\"name\":\"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITNEC.2019.8728992\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNEC.2019.8728992","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Robust Malware Detection System Using Deep Learning on API Calls
With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.