{"title":"基于系统发育的iOS手机恶意软件分类:概念验证","authors":"M. A. Husainiamer, M. Saudi, Azuan Ahmad","doi":"10.1109/ICOS50156.2020.9293666","DOIUrl":null,"url":null,"abstract":"There are raising cases of mobile malwares exploiting iOS users across the world such as FinSpy and Exodus that were able to steal credential information from the victims and affect loss of victims’ productivity. Yet, not many solutions were able to encounter iOS malware attacks. Hence, this paper presents a new iOS mobile malware classification based on mobile behaviour, vulnerability exploitation inspired by phylogenetic concept. The experiment was conducted by using hybrid analysis. Proof of concept (POC) was conducted and based on the POC it indicated that this proposed classification is significant to detect the malware attacks. In future, this proposed classification will be the input for iOS mobile malware detection.","PeriodicalId":314692,"journal":{"name":"2020 IEEE Conference on Open Systems (ICOS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Classification for iOS Mobile Malware Inspired by Phylogenetic: Proof of Concept\",\"authors\":\"M. A. Husainiamer, M. Saudi, Azuan Ahmad\",\"doi\":\"10.1109/ICOS50156.2020.9293666\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There are raising cases of mobile malwares exploiting iOS users across the world such as FinSpy and Exodus that were able to steal credential information from the victims and affect loss of victims’ productivity. Yet, not many solutions were able to encounter iOS malware attacks. Hence, this paper presents a new iOS mobile malware classification based on mobile behaviour, vulnerability exploitation inspired by phylogenetic concept. The experiment was conducted by using hybrid analysis. Proof of concept (POC) was conducted and based on the POC it indicated that this proposed classification is significant to detect the malware attacks. In future, this proposed classification will be the input for iOS mobile malware detection.\",\"PeriodicalId\":314692,\"journal\":{\"name\":\"2020 IEEE Conference on Open Systems (ICOS)\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Conference on Open Systems (ICOS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOS50156.2020.9293666\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Conference on Open Systems (ICOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOS50156.2020.9293666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification for iOS Mobile Malware Inspired by Phylogenetic: Proof of Concept
There are raising cases of mobile malwares exploiting iOS users across the world such as FinSpy and Exodus that were able to steal credential information from the victims and affect loss of victims’ productivity. Yet, not many solutions were able to encounter iOS malware attacks. Hence, this paper presents a new iOS mobile malware classification based on mobile behaviour, vulnerability exploitation inspired by phylogenetic concept. The experiment was conducted by using hybrid analysis. Proof of concept (POC) was conducted and based on the POC it indicated that this proposed classification is significant to detect the malware attacks. In future, this proposed classification will be the input for iOS mobile malware detection.