A flash crowd attack (FCA) floods a service, such as a Web server, with well-formed requests, generated by numerous bots. FCA traffic is difficult to filter, since individual attack and legitimate service requests look identical. We propose robust and reliable models of human interaction with server, which can identify and block a wide variety of bots. We implement the models in a system called FRADE, and evaluate them on three Web servers with different server applications and content. Our results show that FRADE detects both naive and sophisticated bots within seconds, and successfully filters out attack traffic. FRADE significantly raises the bar for a successful attack, by forcing attackers to deploy at least three orders of magnitude larger botnets than today.
https://steel.isi.edu/Projects/frade/
https://ieeexplore.ieee.org/document/8888105
Authors:
PI: Jelena Mirkovic, Genevieve Bartlett
Students: Rajat Tandon, Abhinav Palia, Jaydeep Ramani, Brandon Paulsen
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Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No. 1319215. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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