Network fault detection using immune danger model

Computation spending is great and false detection rate is high using the traditional immune theory based on SNS(Self-NonSelf) Recognition model in the detection of a network composed of multiple test points. Inspired by danger model theory in biological immunology, in this paper, it is proposed a novel immune faults detection algorithm by combining traditional immune algorithm with danger model theory, and this algorithm is applied to a network composed of multiple test points. In this algorithm, a danger signal is considered to be a fault signal by analysis and comprehensive evaluation. The experimental results proved that the algorithm not only simplifies the calculation process, but also has a higher efficiency and low false detection rate.