网络抗入侵容忍度评估模型的设计与仿真

Design and Simulation of Network Anti-Invasiveness Evaluation Model

为了保证网络系统在遭受攻击和入侵时依然能够正常提供基本服务,需要建立一种有效的网络抗入侵容忍度评估模型。当前模型在网络遭遇攻击和入侵后,无法从这些存在安全隐患的网络数据信息流量中区分出可疑信息和可信信息,导致评估模型误差较大。建立了一种基于自律计算和突变理论的网络抗入侵容忍度评估模型,建立了采用一个包括属性层和评估指标层的网络抗入侵容忍度评估模型,确定了评估指标。采用层次分析法构建了系统递阶层次结构和判断矩阵,利用特征根法分别对系统中各个层次包含元素的权重值进行计算和一致性检验,将模型从上到下的单层元素的权重进行合成,获得模型最底层相对于最高层的合成权重,并进行相应参数计算。对系统的容忍势能函数及每个容忍空间的容忍性函数进行计算,推导出网络抗入侵容忍突变点。利用容忍突变点完成对网络抗入侵容忍度评估。仿真结果表明,所提方法能够有效识别出网络入侵数据信息和网络可信数据信息,且评估误差较小。

An evaluation model for network anti-intrusion tolerance based on autonomic computing and mutation theory was established. Firstly, the model including an attribute layer and evaluation index layer was used to deter-mine evaluation index. Then, the analytic hierarchy was used to construct hierarchical structure of system and judgment matrix. Moreover, the characteristic root method was used to calculate and consistently check weight value of elements in each layer of the system, and the weights of single-layer elements from the top to the bottom of the model were integrated to obtain the synthetic weight of the lowest level of model relative to the highest level. The corresponding parameters were calculated. Finally, the tolerance potential function of the system and tolerance function of each tolerant space were calculated to deduce the mutation point of network anti-intrusion tolerance. Finally, the tolerance mutation point was used to complete the evaluation of network antiintrusion tolerance. Simulation results show that the proposed method can effectively identify information of network intrusion data and network reliable data, and the evaluation error is small.