一种基于受体编辑和免疫抑制的人工免疫系统模型

Receptor Editing and Immune Suppression Based Artificial Immune System

被广泛采用的人工免疫系统模型ARTIS中的检测器没有主动学习能力,在具体应用中存在检测半径设定困难、检测性能低等问题,受生物免疫中受体编辑和免疫抑制的启发,提出了一种新的人工免疫系统模型REISAIS(Receptor Editing and Immune Suppression based Artificial Immune System),模型通过受体编辑分别在耐受期和成熟期赋予检测器一定的主动学习能力,从而提高了模型的检测率,而免疫抑制机制的引入则使得模型的误报率得到了有效控制。给出了模型中检测器和抑制器演化过程的形式化描述,对模型性能进行了分析,证明了受体编辑机制的引入在提高模型检测性能上的有效性。理论分析以及实验结果显示,与ARTIS模型相比,REISAIS模型无需设定检测半径并且检测性能更好。

The detector in the model of artificial immune system （ARTIS） has no ability of active learning. It is difficult to set detection radius and makes detection performance slow in specific applications. Inspired by the receptor editing and immune suppression in the theory of biological immune, a new model called REISAIS （Receptor Editing and Im- mune Suppression based Artificial Immune System） was proposed. The model gives the detector a certain degree of ac- tive learning ability through reeeptor editing in the tolerance and mature stages. Thereby, the detection rate of the model is improved. The introduction of the immunosuppressive mechanism makes the false alarm rate of the model to be effee- tivelly controlled. In this paper, the formal description of the detector and suppressor was presented and the performance of the model was analyzed. The effectiveness of receptor editing for improving the detection performance was also proved. Theoretical analysis and experimental results show that the REISAIS achieves better detection performance without setting detection radius compared with ARTIS model.