Robust Self-Monitoring in Trusted Desktop Grids for Self-Configuration at Runtime

Abstract

A Trusted Desktop Grid System (TDG) is a platform for autonomous agents to share their computing resources based on trust relationships. Thereby, agents that only use the system without a fair participation are considered as malicious. Typically, the effects of active malicious agents and high-load situations of the TDG are similar -- calling for appropriate approaches to distinguish them and, thus, allowing for counter measures to attacks. In this paper, we investigate the effect of high load to our measurements and present a concept for filtering our metrics. The evaluation demonstrated that we can normalise our metrics under high load to detect attacks with a high certainty using system-wide metrics.