Andre Wijaya, Charles Lim, Yohanes Syailendra Kotualubun
{"title":"基于API调用分类的恶意软件分类方法","authors":"Andre Wijaya, Charles Lim, Yohanes Syailendra Kotualubun","doi":"10.1145/3557738.3557851","DOIUrl":null,"url":null,"abstract":"The development of malware and computer security countermeasures is in a continuous arms race. Malware authors will adapt their malware according to the current state of events to maximize their chance of success. This increases the value of rapidly detecting the presence of malware within a system and identifying the type of malware. This research proposes a new method of classifying malware using API call categorization based on markov chain. The proposed methods have demonstrated a moderate accuracy of 87.19% with an f-1 score of 75.18%.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Malware Classification Method Using API Call Categorization\",\"authors\":\"Andre Wijaya, Charles Lim, Yohanes Syailendra Kotualubun\",\"doi\":\"10.1145/3557738.3557851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The development of malware and computer security countermeasures is in a continuous arms race. Malware authors will adapt their malware according to the current state of events to maximize their chance of success. This increases the value of rapidly detecting the presence of malware within a system and identifying the type of malware. This research proposes a new method of classifying malware using API call categorization based on markov chain. The proposed methods have demonstrated a moderate accuracy of 87.19% with an f-1 score of 75.18%.\",\"PeriodicalId\":178760,\"journal\":{\"name\":\"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3557738.3557851\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3557738.3557851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Malware Classification Method Using API Call Categorization
The development of malware and computer security countermeasures is in a continuous arms race. Malware authors will adapt their malware according to the current state of events to maximize their chance of success. This increases the value of rapidly detecting the presence of malware within a system and identifying the type of malware. This research proposes a new method of classifying malware using API call categorization based on markov chain. The proposed methods have demonstrated a moderate accuracy of 87.19% with an f-1 score of 75.18%.