Name
Title | ||
|---|---|---|
Learning to control a structured-prediction decoder for detection of HTTP-layer DDoS attackers. |
Abstract | ||
|---|---|---|
We focus on the problem of detecting clients that attempt to exhaust server resources by flooding a service with protocol-compliant HTTP requests. Attacks are usually coordinated by an entity that controls many clients. Modeling the application as a structured-prediction problem allows the prediction model to jointly classify a multitude of clients based on their cohesion of otherwise inconspicuous features. Since the resulting output space is too vast to search exhaustively, we employ greedy search and techniques in which a parametric controller guides the search. We apply a known method that sequentially learns the controller and the structured-prediction model. We then derive an online policy-gradient method that finds the parameters of the controller and of the structured-prediction model in a joint optimization problem; we obtain a convergence guarantee for the latter method. We evaluate and compare the various methods based on a large collection of traffic data of a web-hosting service. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10994-016-5581-9 | Machine Learning |
Keywords | Field | DocType |
DDoS Attackers,Distributed Denial Of Service (DDoS),Predicted Structure Model,Joint Optimization Problem,Support Vector Data Description (SVDD) | Cohesion (chemistry),Convergence (routing),Data mining,Control theory,Denial-of-service attack,Computer science,Structured prediction,Greedy algorithm,Parametric statistics,Artificial intelligence,Optimization problem,Machine learning | Journal |
Volume | Issue | ISSN |
104 | 2-3 | 0885-6125 |
Citations | PageRank | References |
1 | 0.34 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dick, Uwe | 1 | 27 | 2.48 |
| Tobias Scheffer | 2 | 1862 | 139.64 |