MILA – multilevel immune learning algorithm and its application to anomaly detection

Authors:
D. Dasgupta;S. Yu;N. S. Majumdar
Affiliations:
Computer Science Division, Mathematical Sciences Department, University of Memphis, TN 38152, Memphis, USA;Computer Science Division, Mathematical Sciences Department, University of Memphis, TN 38152, Memphis, USA;Computer Science Division, Mathematical Sciences Department, University of Memphis, TN 38152, Memphis, USA
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2005

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Abstract

T-cell-dependent humoral immune response is one of the more complex immunological events in the biological immune system, involving interaction of B cells with antigen (Ag) and their proliferation, differentiation and subsequent secretion of antibody (Ab). Inspired by these immunological principles, a Multilevel Immune Learning Algorithm (MILA) is proposed for novel pattern recognition. This paper describes the detailed background of MILA, and outlines its main features in different phases: Initialization phase, Recognition phase, Evolutionary phase and Response phase. Different test problems are studied and experimented with MILA for performance evaluation. The results show MILA is flexible and efficient in detecting anomalies and novel patterns.