{"title":"New sensing technique for detecting application layer DDoS attacks targeting back-end database resources","authors":"D. Beckett, S. Sezer, J. McCanny","doi":"10.1109/ICC.2017.7997376","DOIUrl":null,"url":null,"abstract":"Distributed Denial of Service (DDoS) attacks targeting the application layer are becoming more prevalent due to a lack of suitable defence solutions. Existing research treats the web server environment as a black box, by only monitoring the edge network traffic; however, we believe that this approach limits the accuracy of the detection system as it does not protect the back-end database servers. In this paper we propose a new sensor located within the back-end system, which can produce additional database features. This allows for real-time insight into the actual database workload caused by each user enabling the detection of DDoS attacks targeting high database consumption resources. These resource metrics are analysed in real-time on a live website, using a decision tree classification engine. Our preliminary results show that a low rate asymmetric attack as low as 1 request every 10 seconds can be detected using these proposed features.","PeriodicalId":6517,"journal":{"name":"2017 IEEE International Conference on Communications (ICC)","volume":"255 1","pages":"1-7"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Communications (ICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICC.2017.7997376","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Distributed Denial of Service (DDoS) attacks targeting the application layer are becoming more prevalent due to a lack of suitable defence solutions. Existing research treats the web server environment as a black box, by only monitoring the edge network traffic; however, we believe that this approach limits the accuracy of the detection system as it does not protect the back-end database servers. In this paper we propose a new sensor located within the back-end system, which can produce additional database features. This allows for real-time insight into the actual database workload caused by each user enabling the detection of DDoS attacks targeting high database consumption resources. These resource metrics are analysed in real-time on a live website, using a decision tree classification engine. Our preliminary results show that a low rate asymmetric attack as low as 1 request every 10 seconds can be detected using these proposed features.