Network Fault Diagnosis: An Artificial Immune System Approach

Abstract

Artificial immune systems (AIS) have been widely used in many fields such as data analysis, multimodal function optimization, error detection, etc. In this paper, we introduce a novel artificial immune systems approach for diagnosing faults in a network of processors under the PMC model. We investigate how AIS can be used for system-level fault diagnosis. Our theoretical analysis and experimental results demonstrate the effectiveness of the AIS-based diagnosis approach for small and large class of networks in both the worst and average cases, making it a viable alternative to traditional fault diagnosis approaches.