Jun Xu, Pinyao Guo, Bo Chen, R. Erbacher, Ping Chen, Peng Liu
{"title":"演示:符号n变系统","authors":"Jun Xu, Pinyao Guo, Bo Chen, R. Erbacher, Ping Chen, Peng Liu","doi":"10.1145/2995272.2995284","DOIUrl":null,"url":null,"abstract":"This demo paper describes an approach to detect memory corruption attacks using artificial diversity. Our approach conducts offline symbolic execution of multiple variants of a system to identify paths which diverge in different variants. In addition, we build an efficient input matcher to check whether an online input matches the constraints of a diverging path, to detect potential malicious input. By evaluating the performance of a demo system built on Ghttpd, we find that per-input matching consumes only 70% to 96% of the real processing time in the master, which indicates a performance superiority for real world deployment.","PeriodicalId":20539,"journal":{"name":"Proceedings of the 2016 ACM Workshop on Moving Target Defense","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Demo: A Symbolic N-Variant System\",\"authors\":\"Jun Xu, Pinyao Guo, Bo Chen, R. Erbacher, Ping Chen, Peng Liu\",\"doi\":\"10.1145/2995272.2995284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This demo paper describes an approach to detect memory corruption attacks using artificial diversity. Our approach conducts offline symbolic execution of multiple variants of a system to identify paths which diverge in different variants. In addition, we build an efficient input matcher to check whether an online input matches the constraints of a diverging path, to detect potential malicious input. By evaluating the performance of a demo system built on Ghttpd, we find that per-input matching consumes only 70% to 96% of the real processing time in the master, which indicates a performance superiority for real world deployment.\",\"PeriodicalId\":20539,\"journal\":{\"name\":\"Proceedings of the 2016 ACM Workshop on Moving Target Defense\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 ACM Workshop on Moving Target Defense\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2995272.2995284\",\"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 2016 ACM Workshop on Moving Target Defense","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2995272.2995284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This demo paper describes an approach to detect memory corruption attacks using artificial diversity. Our approach conducts offline symbolic execution of multiple variants of a system to identify paths which diverge in different variants. In addition, we build an efficient input matcher to check whether an online input matches the constraints of a diverging path, to detect potential malicious input. By evaluating the performance of a demo system built on Ghttpd, we find that per-input matching consumes only 70% to 96% of the real processing time in the master, which indicates a performance superiority for real world deployment.
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