RT Journal Article
T1 Entropy-Based Economic Denial of Sustainability Detection
A1 Sotelo Monge, Marco Antonio
A1 Maestre Vidal, Jorge
A1 García Villalba, Luis Javier
AB In recent years, an important increase in the amount and impact of Distributed Denial of Service (DDoS) threats has been reported by the different information security organizations. They typically target the depletion of the computational resources of the victims, hence drastically harming their operational capabilities. Inspired by these methods, Economic Denial of Sustainability (EDoS) attacks pose a similar motivation, but adapted to Cloud computing environments, where the denial is achieved by damaging the economy of both suppliers and customers. Therefore, the most common EDoS approach is making the offered services unsustainable by exploiting their auto-scaling algorithms. In order to contribute to their mitigation, this paper introduces a novel EDoS detection method based on the study of entropy variations related with metrics taken into account when deciding auto-scaling actuations. Through the prediction and definition of adaptive thresholds, unexpected behaviors capable of fraudulently demand new resource hiring are distinguished. With the purpose of demonstrate the effectiveness of the proposal, an experimental scenario adapted to the singularities of the EDoS threats and the assumptions driven by their original definition is described in depth. The preliminary results proved high accuracy.
PB MDPI
SN 1099-4300
YR 2017
FD 2017-11-29
LK https://hdl.handle.net/20.500.14352/19213
UL https://hdl.handle.net/20.500.14352/19213
LA eng
NO Unión Europea. Horizonte 2020
DS Docta Complutense
RD 10 abr 2025