基于一维元胞自动机的复杂网络恶意软件传播研究

基于一维元胞自动机，研究复杂网络恶意软件传播行为.利用信息网络节点全局交互的特点，建立元胞自动机邻域和状态转换函数，提出恶意软件传播模型，研究在多种网络拓扑下恶意软件传播的概率行为.研究表明，该模型能够准确描述在最近邻耦合网络（nearest-neighbor coupled network, NC），Erdos-Renyi（ER）随机网络，Watts-Strogatz（WS） 小世界网络和Barabasi-Albert（BA）幂率网络等拓扑下的传播动力学行为，不仅能反映恶意软件传播的平均趋势，而且可以描述病毒消亡和渗透等稀有概率事件，有效克服基于平均场方法建立的微分方程模型只能反映传播的平均趋势，只适合对传播作整体预测的局限性.同时，研究指出网络中度分布的异质化程度和网络的局域空间交互特征是影响传播及免疫行为的关键要素. 关键词： 复杂网络 恶意软件传播 元胞自动机 状态转换函数

Research of malware propagation in complex networks based on 1-D cellular automata

In this paper, based on 1-D cellular automata, the probabilistic behaviors of malware propagation in complex networks are investigated. Neighborhood and state transition functions with integrated expression are established and two models of malware propagation are proposed to evaluate the probabilistic behavior of malware propagation in various networks. We run the proposed models on nearest-neighbor coupled network （NC） and Erdos-Renyi （ER） random graph network and Watts-Strogatz（WS） small world network and Barabasi-Albert （BA） power law network respectively. Analysis and simulations show that, the proposed models describe perfectly the dynamic behaviors of propagation in the above networks. Furthermore, the proposed models describe not only the average tendency of malware propagation but also the rare events such as saturation and extinction of malware, and overcome the limitation occurring in a deterministic model based on mean-field method that describes only the average tendency of malware propagation and neglects the probabilistic event. Meanwhile, the result of simulations shows that the heterogeneity of degree distribution and local spatial interaction are key factors affecting the malware propagation and immunization.