模块化免疫神经网络平衡态检测模型

Research on equilibrium state in modularization immunity neural network model

基于神经网络原理、免疫系统和遗传算法的相关机理,构造了一个网络安全平衡器。该平衡器建立了抗原与抗体平衡态检测的数学模型、抗原与抗体的促进和抑制函数,提出了安全平衡态的概念,给出了抗原与抗体浓度的计算公式和模块化多层分类处理模型,利用促进、抑制函数和遗传算法适应度函数使已受攻击的网络再次达到一种新的安全平衡状态,这为网络安全提供了一个新的途径。理论证明网络安全方案是有效的。

Based on the relevant mechanisms of neural network,immune system and genetic algorithm,this paper established a network security balancer,which built a mathematical model to detect the equilibrium state of antigen and antibody,presented a promotion and inhibition function of antigen and antibody,put forward the concept of security equilibrium state and provided the formula of antigen and antibody concentration and the multi-classification model of modularization.With the promotion and inhibition function and genetic algorithm fitness function,the attacked network could be promoted to a new security equilibrium state,which provided a new solution for the network security.Therefore theoretically proves the efficiency of the network security solution.