A New Probabilistic Mobile Byzantine Failure Model for Self-Protecting Systems

TL;DR

This paper introduces a probabilistic Mobile Byzantine Failure model for self-protecting distributed systems, capturing attack dynamics and aiding in system resilience through analysis and simulation.

Contribution

It proposes a novel probabilistic MBF model integrated into the analysis component of a self-protecting system, enabling dynamic attack modeling and system recovery analysis.

Findings

Mathematical analysis of Byzantine node thresholds

Simulation results demonstrating system behavior

Insights into attack spread and recovery rates

Abstract

Modern distributed systems face growing security threats, as attackers continuously enhance their skills and vulnerabilities span across the entire system stack, from hardware to the application layer. In the system design phase, fault tolerance techniques can be employed to safeguard systems. From a theoretical perspective, an attacker attempting to compromise a system can be abstracted by considering the presence of Byzantine processes in the system. Although this approach enhances the resilience of the distributed system, it introduces certain limitations regarding the accuracy of the model in reflecting real-world scenarios. In this paper, we consider a self-protecting distributed system based on the \emph{Monitoring-Analyse-Plan-Execute over a shared Knowledge} (MAPE-K) architecture, and we propose a new probabilistic Mobile Byzantine Failure (MBF) that can be plugged into the Analysis component. Our new model captures the dynamics of evolving attacks and can be used to drive the self-protection and reconfiguration strategy. We analyze mathematically the time that it takes until the number of Byzantine nodes crosses given thresholds, or for the system to self-recover back into a safe state, depending on the rates of Byzantine infection spreading \emph{vs.} the rate of self-recovery. We also provide simulation results that illustrate the behavior of the system under such assumptions.