Fine-grained tracking of Grid infections

Abstract

Previous distributed anomaly detection efforts have operated on summary statistics gathered from each node. This has the advantage that the audit trail is limited in size since event sets can be succinctly represented. While this minimizes the bandwidth consumed and helps scale the detection to a large number of nodes, it limits the infrastructure's ability to identify the source of anomalies. We describe three optimizations that together allow fine-grained tracking of the sources of anomalous activity in a Grid, thereby facilitating precise responses. We demonstrate the scheme's scalability in terms of storage and network bandwidth overhead with an implementation on nodes running BOINC. The results generalize to other types of Grids as well.