Optimizing Anomaly Detection with Immune System-Inspired Antibody Shapes and Data Transformation Strategies

This study delves into the enhancement of anomalydetection mechanisms inspired by the biological immune system,focusing on the role of antibody morphology in improvingdetection accuracy. Conventional approaches use binary or m-arydata representations, but biological recognition mechanisms areinherently more complex, involving three-dimensional antibodyantigen interactions. We propose novel strategies by modifyingthe structure and ordering of artificial antibodies. First, a fuzzyset-based representation is introduced, which more effectivelyhandles continuous and complex data patterns, unlike traditional discrete representations. Additionally, we examine theinfluence of antigen data reordering, inspired by the MajorHistocompatibility Complex (MHC), on improving classificationperformance and minimizing gaps in the antigen space. Throughempirical analysis of well-known UCI machine learning datasets,we demonstrate how these innovative modifications significantlyenhance the ability of anomaly detection algorithms to classifynon-self data accurately. This research offers valuable insightsinto the design of more adaptable and efficient immune-inspiredalgorithms for modern anomaly detection tasks.