A hierarchical peer-to-peer(P2P) model and a data fusion method for network security situation awareness system are proposed to improve the efficiency of distributed security behavior monitoring network. The single point failure of data analysis nodes is avoided by this P2P model, in which a greedy data forwarding method based on node priority and link delay is devised to promote the efficiency of data analysis nodes. And the data fusion method based on repulsive theory-Dumpster/Shafer(PSORT-DS) is used to deal with the challenge of multi-source alarm information. This data fusion method debases the false alarm rate. Compared with improved Dumpster/Shafer(DS) theoretical method based on particle swarm optimization(PSO) and classical DS evidence theoretical method, the proposed model reduces false alarm rate by 3% and 7%,respectively, whereas their detection rate increases by 4% and 16%, respectively.
机会社会网络(opportunistic social networks)能够利用节点移动创造的相遇机会,在缺乏持续端到端连接的网络中,为用户提供稳定的消息分发途径,但在消息分发效率以及用户体验方面存在不足.为提高消息分发系统的性能、改善网络用户体验,提出一种基于节点兴趣匹配的机会社会网络分发机制.通过引入混合结构的机会社会网络分发系统解决网络拓扑信息获取不全与节点计算能力不足的问题;从节点行为规律与兴趣爱好2方面对网络进行分析,并提出一种用于复杂关系数据分析的联合聚类方法;针对用户需求,设计消息属性与节点兴趣匹配优先的消息分发策略.仿真结果表明,该机制能够在投递率、投递时延、缓存占用率等方面提升网络性能,且具有较高的分发效率、覆盖率与兴趣匹配度.
