Evaluation of Application Layer DDoS Attack Effect in Cloud Native Applications
Cloud native application is especially susceptible to application layer DDoS attack. This attributes to the internal service calls, by which microservices cooperate and communicate with each other, amplifying the effect of application layer DDoS attack. Since different services have varying degrees...
Gespeichert in:
Veröffentlicht in: | IEEE transactions on cloud computing 2024-04, Vol.12 (2), p.522-538 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Cloud native application is especially susceptible to application layer DDoS attack. This attributes to the internal service calls, by which microservices cooperate and communicate with each other, amplifying the effect of application layer DDoS attack. Since different services have varying degrees of sensitivity to an attack, a sophisticated attacker can take advantage of those especially expensive API calls to produce serious damage to the availability of services and applications with ease. To better analyze the severity of and mitigate application layer DDoS attacks in cloud native applications, we propose a novel method to evaluate the effect of application layer DDoS attack, that is able to quantitatively characterize the amplifying effect introduced by the complex structure of application system. We first present the descriptive model of the scenario. Then, Riemannian manifolds are constructed as the state spaces of the attack scenarios, in which attacks are described as homeomorphisms. Finally, we apply differential geometry principles to quantitatively calculate the attack effect, which is derived from the action of an attack and the movement it produces in the state spaces. The proposed method is validated in various application scenarios. We show that our approach provides accurate evaluation results, and outperforms existing solutions. |
---|---|
ISSN: | 2168-7161 2168-7161 2372-0018 |
DOI: | 10.1109/TCC.2024.3374798 |