基于信任的物联网感知节点安全成簇机制研究

发布时间：2018-06-17 04:36

本文选题：物联网 + 安全成簇　；参考：《重庆邮电大学》2014年硕士论文

【摘要】：近年来，物联网已经在军事侦察、智能电网等多个基础领域得到广泛应用，而这些应用中包含的海量数据与群体和个人的隐私及保密问题有关。但物联网是一个开放的环境，定义安全边界困难。此外，负责信息采集的感知节点能力脆弱，资源有限，并分布在无人监管的环境中，容易遭受恶意攻击。而传统数据融合技术把过多关注数据融合的效率，忽略了节点行为的信任问题。其次，已有的加密认证方法计算过程复杂，而且不能解决来自感知节点的内部攻击。因此，思考如何保障物联网的感知信息安全，特别是围绕感知节点安全高效成簇机制及算法的研究成为物联网进一步发展不可或缺的关键需求。论文分析典型成簇算法的不足：簇头节点的选择没有考虑节点的剩余能量，不能识别和抵御恶意攻击。此外，所有感知节点周期性地执行成簇的操作将会产生大量能耗。因此，论文给出一种基于信任的节点安全成簇(Trust-based Secure ClusteringProtocol，TBSCP)方案。TBSCP以给出的信任评估方法为基础，对冒充或伪装成正常节点参与数据融合的恶意节点进行识别与过滤，并在节点状态发生变化的区域内重新选择簇头。主要的工作有：（1）给出基于节点行为检测的信任评估方法，该方法采用事件触发与周期性检测相结合的方式，可以实现节点行为的实时监测，将信任值取整并加入惩罚机制，减少信任记录并缩短发现恶意节点的时间；（2）在信任评估方法的基础上，给出一种新的推荐信任合并规则，该规则根据证据距离不断修正推荐信任值，可在不增加计算复杂的前提下，提高推荐信任合并结果的准确性，有效地抵御恶意节点的诽谤攻击；（3）给出基于信任的节点安全成簇TBSCP方案，该方案对数据融合过程的非正常节点进行检测，并在汇聚节点状态发生变化的区域内重选簇头。仿真实验证明，，给出的TBSCP方案较高的安全性，加入的信任评估方法有较低的能耗性。
[Abstract]:In recent years, the Internet of things has been widely used in military reconnaissance, smart grid and other basic fields, and these applications contain huge amounts of data related to the privacy and privacy of groups and individuals. But the Internet of things is an open environment and it is difficult to define secure borders. In addition, the perceptual nodes in charge of information collection are vulnerable to malicious attacks due to their weak capability, limited resources and distribution in unsupervised environments. The traditional data fusion technology pays too much attention to the efficiency of data fusion and ignores the trust problem of node behavior. Secondly, the existing encryption authentication methods are complex and can not solve the internal attacks from perceptual nodes. Therefore, thinking about how to ensure the security of perceptual information in the Internet of things, especially the research of clustering mechanism and algorithm around the security and efficiency of perceptual nodes, has become an indispensable key demand for the further development of the Internet of things. This paper analyzes the shortcomings of typical clustering algorithms: the selection of cluster head nodes does not take into account the residual energy of the nodes and can not identify and resist malicious attacks. In addition, all perceptual nodes periodically perform clustering operations that result in a large amount of energy consumption. Therefore, this paper presents a Trust-based secure clustering TBSCP scheme based on trust. TBSCP identifies and filters malicious nodes posing as normal nodes to participate in data fusion based on the trust evaluation method given. The cluster head is re-selected in the region where the node state changes. The main work is as follows: (1) A trust evaluation method based on node behavior detection is presented. The method combines event trigger and periodic detection to realize real-time monitoring of node behavior, rounding the trust value and adding punishment mechanism. On the basis of trust evaluation method, a new recommended trust merge rule is proposed, which constantly modifies the recommended trust value according to the distance of evidence. We can improve the accuracy of the recommended trust merge results without increasing the computational complexity, and effectively resist the malicious node defamation attacks. (3) A trusted node security cluster TBSCP scheme is presented. The scheme detects the abnormal nodes in the data fusion process and resets the cluster heads in the regions where the state of the convergent nodes changes. Simulation results show that the proposed TBSCP scheme has high security and low energy consumption.
【学位授予单位】：重庆邮电大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP391.44;TN915.08

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