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Auton. Adapt. Syst."],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>Denial of Service (DoS) attacks overwhelm online services, preventing legitimate users from accessing a service, often with impact on revenue or consumer trust. Approaches exist to filter network-level attacks, but application-level attacks are harder to detect at the firewall. Filtering at this level can be computationally expensive and difficult to scale, while still producing false positives that block legitimate users.<\/jats:p>\n          <jats:p>This article presents a model-based adaptive architecture and algorithm for detecting DoS attacks at the web application level and mitigating them. Using a performance model to predict the impact of arriving requests, a decision engine adaptively generates rules for filtering traffic and sending suspicious traffic for further review, where the end user is given the opportunity to demonstrate they are a legitimate user. If no legitimate user responds to the challenge, the request is dropped. Experiments performed on a scalable implementation demonstrate effective mitigation of attacks launched using a real-world DoS attack tool.<\/jats:p>","DOI":"10.1145\/2567926","type":"journal-article","created":{"date-parts":[[2014,3,24]],"date-time":"2014-03-24T13:45:50Z","timestamp":1395668750000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Mitigating DoS Attacks Using Performance Model-Driven Adaptive Algorithms"],"prefix":"10.1145","volume":"9","author":[{"given":"Cornel","family":"Barna","sequence":"first","affiliation":[{"name":"York University"}]},{"given":"Mark","family":"Shtern","sequence":"additional","affiliation":[{"name":"York University"}]},{"given":"Michael","family":"Smit","sequence":"additional","affiliation":[{"name":"Dalhousie University"}]},{"given":"Vassilios","family":"Tzerpos","sequence":"additional","affiliation":[{"name":"York University"}]},{"given":"Marin","family":"Litoiu","sequence":"additional","affiliation":[{"name":"York University"}]}],"member":"320","published-online":{"date-parts":[[2014,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"APERA. 2009. 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