Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering

Markus Goldstein, Christoph Lampert, Matthias Reif, Armin Stahl, Thomas Breuel
Seventh International Conference on Networking, Pages 174-179, Cancun, Mexico, IEEE Computer Society, 4/2008

Abstract:

Distributed Denial of Service (DDoS) attacks are today the most destabilizing factor in the global internet and there is a strong need for sophisticated solutions. We introduce a formal statistical framework and derive a Bayes optimal packet classifier from it. Our proposed practical algorithm "Adaptive History-Based IP Filtering" (AHIF) mitigates DDoS attacks near the victim and outperforms existing methods by at least 32% in terms of collateral damage. Furthermore, it adjusts to the strength of an ongoing attack and ensures availability of the attacked server. In contrast to other adaptive solutions, firewall rulesets used to resist an attack can be precalculated before an attack takes place. This ensures an immediate response in a DDoS emergency. For evaluation, simulated DDoS attacks and two real-world user traffic datasets are used.

Files:

  Bayes_optimal_DDoS_Mitigation.pdf
  Talk.pdf

BibTex:

@inproceedings{ GOLD2008,
	Title = {Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering},
	Author = {Markus Goldstein and Christoph Lampert and Matthias Reif and Armin Stahl and Thomas Breuel},
	BookTitle = {Seventh International Conference on Networking},
	Month = {4},
	Year = {2008},
	Publisher = {IEEE Computer Society},
	Pages = {174-179}
}

     
Last modified:: 30.08.2016