本文選題：物聯(lián)網(wǎng) + 安全成簇�。� 參考：《重慶郵電大學(xué)》2014年碩士論文

【摘要】：近年來，物聯(lián)網(wǎng)已經(jīng)在軍事偵察、智能電網(wǎng)等多個基礎(chǔ)領(lǐng)域得到廣泛應(yīng)用，而這些應(yīng)用中包含的海量數(shù)據(jù)與群體和個人的隱私及保密問題有關(guān)。但物聯(lián)網(wǎng)是一個開放的環(huán)境，定義安全邊界困難。此外，負責(zé)信息采集的感知節(jié)點能力脆弱，資源有限，并分布在無人監(jiān)管的環(huán)境中，容易遭受惡意攻擊。而傳統(tǒng)數(shù)據(jù)融合技術(shù)把過多關(guān)注數(shù)據(jù)融合的效率，忽略了節(jié)點行為的信任問題。其次，已有的加密認證方法計算過程復(fù)雜，而且不能解決來自感知節(jié)點的內(nèi)部攻擊。因此，思考如何保障物聯(lián)網(wǎng)的感知信息安全，特別是圍繞感知節(jié)點安全高效成簇機制及算法的研究成為物聯(lián)網(wǎng)進一步發(fā)展不可或缺的關(guān)鍵需求。 論文分析典型成簇算法的不足：簇頭節(jié)點的選擇沒有考慮節(jié)點的剩余能量，不能識別和抵御惡意攻擊。此外，所有感知節(jié)點周期性地執(zhí)行成簇的操作將會產(chǎn)生大量能耗。因此，論文給出一種基于信任的節(jié)點安全成簇(Trust-based Secure ClusteringProtocol，TBSCP)方案。TBSCP以給出的信任評估方法為基礎(chǔ)，對冒充或偽裝成正常節(jié)點參與數(shù)據(jù)融合的惡意節(jié)點進行識別與過濾，并在節(jié)點狀態(tài)發(fā)生變化的區(qū)域內(nèi)重新選擇簇頭。主要的工作有：（1）給出基于節(jié)點行為檢測的信任評估方法，該方法采用事件觸發(fā)與周期性檢測相結(jié)合的方式，可以實現(xiàn)節(jié)點行為的實時監(jiān)測，將信任值取整并加入懲罰機制，減少信任記錄并縮短發(fā)現(xiàn)惡意節(jié)點的時間；（2）在信任評估方法的基礎(chǔ)上，給出一種新的推薦信任合并規(guī)則，該規(guī)則根據(jù)證據(jù)距離不斷修正推薦信任值，可在不增加計算復(fù)雜的前提下，提高推薦信任合并結(jié)果的準確性，有效地抵御惡意節(jié)點的誹謗攻擊；（3）給出基于信任的節(jié)點安全成簇TBSCP方案，該方案對數(shù)據(jù)融合過程的非正常節(jié)點進行檢測，并在匯聚節(jié)點狀態(tài)發(fā)生變化的區(qū)域內(nèi)重選簇頭。仿真實驗證明，，給出的TBSCP方案較高的安全性，加入的信任評估方法有較低的能耗性。
[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.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.44;TN915.08

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