AnKLe: Detecting Attacks in Large Scale Systems via Information Divergence

Emmanuelle Anceaume (1, 2) , Yann Busnel (3) , Sébastien Gambs (1, 2)

1 CIDER
2 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
3 LINA - Laboratoire d'Informatique de Nantes Atlantique

In this paper, we consider the setting of large scale distributed systems, in which each node needs to quickly process a huge amount of data received in the form of a stream that may have been tampered with by an adversary. In this situation, a fundamental problem is how to detect and quantify the amount of work performed by the adversary. To address this issue, we propose AnKLe (for Attack-tolerant eNhanced Kullback- Leibler divergence Estimator), a novel algorithm for estimating the KL divergence of an observed stream compared to the expected one. AnKLe com- bines sampling techniques and information-theoretic methods. It is very efficient, both in terms of space and time complexities, and requires only a single pass over the data stream. Experimental results show that the estimation provided by AnKLe remains accurate even for different adversarial settings for which the quality of other methods dramatically decreases.

Mots clés: Data Stream; Kullback-Leibler Diver- gence; Sampling; Byzantine Adversary; Scalability; Performance Analysis

Domaines: Calcul parallèle, distribué et partagé [cs.DC]; Théorie de l'information [cs.IT]; Théorie de l'information et codage [math.IT]

Emmanuelle Anceaume, Yann Busnel, Sébastien Gambs. AnKLe: Detecting Attacks in Large Scale Systems via Information Divergence. Ninth European Dependable Computing Conference (EDCC 2012), May 2012, Sibiu, Romania. pp.12. ⟨hal-00677077⟩