A normal model and an immune computation model were modelled to detect recognize and eliminate worms in a static Web system. Immune computation included detecting, recognizing, learning and eliminating non-selfs. The self/non-self detection was based on querying in the self database and the self database was built on the normal model of the static Web system. After the detection, the recognition of known non-self was based on querying in the non-self database and the recognition of unknown non-self was based on learning unknown non-self. The learning algorithm was designed on the neural network or the learning mechanism from examples. The last step was elimination of all the non-self and failover of the damaged Web system. The immunization of the static Web system was programmed with Java to test effectiveness of the approach. Some worms infected the static Web system, and caused the abnormity. The results of the immunization simulations show that, the immune program can detect all worms, recognize known worms and most unknown worms, and eliminate the worms. The damaged files of the static Web system can all be repaired through the normal model and immunization. The normal model & immune computation model are effective in some anti-worm applications.