Artificial Immunity-Inspired Script-Virus Detection Model

To detect increasing mutated script-virus, an artificial immunity-based script-virus detection model is presented. Self-tolerance and clone selection mechanism in artificial immune system are used for reference. Script-code is presented to antigen. Antibody simulates detector in the environment of virus detection. Antibody is classified according to immunity learning mechanism. The negative selection algorithm is simulated to avoid recognizing normal antigen. Self-learning of antibody is used to discover harmful mutated antigens. Mature and memory antibody are evolved to set up dynamic production and elimination mechanism of antibody to decrease false detection rate. The simulation experiment shows that proposed model is able to detect mutated script-virus and provides a new way to detect script-virus.