融合信任值和身份标识验证的节点复制攻击检测策略

Node replication attack detection strategy integrating node creditworthiness and identity authentication

关键词:  无线传感器网络  信任值  身份标识验证  节点复制攻击  模糊权重

Key words:  wireless sensor network  trust value  identity verification  node replication attack  fuzzy weight

摘要:

为抵御无线传感器网络中的节点复制攻击,保障网络的安全与稳定,本文提出了一种融合信任值和身份标识验证的节点复制攻击检测策略(node replication attack detection strategy integrating node creditworthiness and identity authentication,NRADS-NC＆IA)。该方法首先建立模糊综合信任评价模型,在直接信任模块中引入信誉维护函数、异常弱化因子及奖惩因子,综合通信属性、数据属性、网络属性和物理属性影响因素计算待评估节点的直接信任值,并采用滑动时间窗口机制实现节点信任值的动态更新,有效提升评估的时效性与准确性,在此基础上应用支持度函数评估节点可信度,从而有效过滤节点的欺骗行为得到更为可靠的节点间接信任值,最终的节点综合信任值由直接信任值与间接信任值加权求和得到；采用动态自适应阈值筛选可疑节点,并在可疑节点中进行节点ID比对,以确定副本节点。仿真实验表明,NRADS-NC＆IA在无需依赖节点空间位置信息的情形下,静态和移动无线传感器网络的检测率保持在97%和94%以上,具有较强的环境适应性,可有效应对复杂动态环境中的无线传感器网络安全问题。

Abstract:

To defend against node replication attacks in wireless sensor networks and maintain network security and stability, this paper proposes a node replication attack detection strategy integrating node creditworthiness and identity authentication (NRADS-NC＆IA). The approach begins by establishing a fuzzy comprehensive trust evaluation model. In the direct trust module, reputation maintenance function, anomaly attenuation factor and reward and punishment factor are introduced. The direct trust value of the node to be evaluated is calculated by comprehensively considering the influencing factors of communication attributes, data attributes, network attributes and physical attributes. A sliding time window mechanism is adopted to dynamically update node trust value, significantly enhancing the timeliness and accuracy of the evaluation. On this basis, a support function is applied to evaluate the credibility of nodes, effectively filtering out the deceptive behaviors of nodes and obtaining more reliable indirect trust values of nodes. The final comprehensive trust value of nodes is derived from the weighted summation of its direct and indirect trust values. Dynamic adaptive thresholds are adopted to screen suspicious nodes, and node ID comparisons are conducted among the suspicious nodes to obtain the determined replica nodes. Simulation results show that NRADS-NC&IA achieves detection rates of over 97% and 94% in static and mobile wireless sensor networks without relying on the spatial position information of nodes. The strategy exhibits strong environmental adaptability and can effectively deal with the security problems of wireless sensor networks in complex dynamic environments.