{"title":"最终用户可以更快地缓解零日攻击","authors":"Vivek Bardia, Crs Kumar","doi":"10.1109/IACC.2017.0190","DOIUrl":null,"url":null,"abstract":"The past decade has shown us the power of cyberspace and we getting dependent on the same. The exponentialevolution in the domain has attracted attackers and defenders oftechnology equally. This inevitable domain has led to the increasein average human awareness and knowledge too. As we see theattack sophistication grow the protectors have always been a stepahead mitigating the attacks. A study of the various ThreatDetection, Protection and Mitigation Systems revealed to us acommon similarity wherein users have been totally ignored or thesystems rely heavily on the user inputs for its correct functioning. Compiling the above we designed a study wherein user inputswere taken in addition to independent Detection and Preventionsystems to identify and mitigate the risks. This approach led us toa conclusion that involvement of users exponentially enhancesmachine learning and segments the data sets faster for a morereliable output.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"69 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"End Users Can Mitigate Zero Day Attacks Faster\",\"authors\":\"Vivek Bardia, Crs Kumar\",\"doi\":\"10.1109/IACC.2017.0190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The past decade has shown us the power of cyberspace and we getting dependent on the same. The exponentialevolution in the domain has attracted attackers and defenders oftechnology equally. This inevitable domain has led to the increasein average human awareness and knowledge too. As we see theattack sophistication grow the protectors have always been a stepahead mitigating the attacks. A study of the various ThreatDetection, Protection and Mitigation Systems revealed to us acommon similarity wherein users have been totally ignored or thesystems rely heavily on the user inputs for its correct functioning. Compiling the above we designed a study wherein user inputswere taken in addition to independent Detection and Preventionsystems to identify and mitigate the risks. This approach led us toa conclusion that involvement of users exponentially enhancesmachine learning and segments the data sets faster for a morereliable output.\",\"PeriodicalId\":248433,\"journal\":{\"name\":\"2017 IEEE 7th International Advance Computing Conference (IACC)\",\"volume\":\"69 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 7th International Advance Computing Conference (IACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IACC.2017.0190\",\"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 IEEE 7th International Advance Computing Conference (IACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IACC.2017.0190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The past decade has shown us the power of cyberspace and we getting dependent on the same. The exponentialevolution in the domain has attracted attackers and defenders oftechnology equally. This inevitable domain has led to the increasein average human awareness and knowledge too. As we see theattack sophistication grow the protectors have always been a stepahead mitigating the attacks. A study of the various ThreatDetection, Protection and Mitigation Systems revealed to us acommon similarity wherein users have been totally ignored or thesystems rely heavily on the user inputs for its correct functioning. Compiling the above we designed a study wherein user inputswere taken in addition to independent Detection and Preventionsystems to identify and mitigate the risks. This approach led us toa conclusion that involvement of users exponentially enhancesmachine learning and segments the data sets faster for a morereliable output.