Using positive and negative from immunology for detection of anomalies in a self-protecting middleware

Abstract

The immune system of our body matured over millions of years and has proven ist functionality. In our research we are going to transfer techniques of a biological immune system to a computer based environment. Our goal is to design a self-protecting middleware which isn't vulnerable to malicious messages. First off this paper proposes an artificial immune system with negative and positive selection and evaluates optimal parameter settings. Our tests showded that the detection rate of unknown malicious messages can reach up to 99.6%. Further on we propose techniques to minimize the memory space needed for storing the antibodies and to speedup the time needed for detecting malicious messages. We obtained a space minimization by 30% and gained a speedup of 30 with execution time optimization.