{"title":"基于word2vec嵌入机器代码指令的机器学习恶意软件检测","authors":"I. Popov","doi":"10.1109/SSDSE.2017.8071952","DOIUrl":null,"url":null,"abstract":"Applying machine learning for automatic malware detection is a perspective field of scientific research. One of popular methods in static analysis of executable files is observing machine code instructions that they contain. This paper proposes applying word2vec technique for extracting vector embeddings of machine code instructions and evaluates convolutional neural network-based classifier that uses extracted vectors for malware detection.","PeriodicalId":216748,"journal":{"name":"2017 Siberian Symposium on Data Science and Engineering (SSDSE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":"{\"title\":\"Malware detection using machine learning based on word2vec embeddings of machine code instructions\",\"authors\":\"I. Popov\",\"doi\":\"10.1109/SSDSE.2017.8071952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Applying machine learning for automatic malware detection is a perspective field of scientific research. One of popular methods in static analysis of executable files is observing machine code instructions that they contain. This paper proposes applying word2vec technique for extracting vector embeddings of machine code instructions and evaluates convolutional neural network-based classifier that uses extracted vectors for malware detection.\",\"PeriodicalId\":216748,\"journal\":{\"name\":\"2017 Siberian Symposium on Data Science and Engineering (SSDSE)\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"30\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Siberian Symposium on Data Science and Engineering (SSDSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSDSE.2017.8071952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Siberian Symposium on Data Science and Engineering (SSDSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSDSE.2017.8071952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Malware detection using machine learning based on word2vec embeddings of machine code instructions
Applying machine learning for automatic malware detection is a perspective field of scientific research. One of popular methods in static analysis of executable files is observing machine code instructions that they contain. This paper proposes applying word2vec technique for extracting vector embeddings of machine code instructions and evaluates convolutional neural network-based classifier that uses extracted vectors for malware detection.