本文選題：機(jī)會網(wǎng)絡(luò)　切入點(diǎn)：信任模型　出處：《湖南科技大學(xué)》2017年碩士論文

【摘要】：機(jī)會網(wǎng)絡(luò)是一種不需要源節(jié)點(diǎn)和目的節(jié)點(diǎn)之間存在完整路徑,利用節(jié)點(diǎn)移動帶來的相遇機(jī)會實(shí)現(xiàn)網(wǎng)絡(luò)通信的自組織網(wǎng)絡(luò)。而機(jī)會網(wǎng)絡(luò)中如果存在自私節(jié)點(diǎn)或惡意節(jié)點(diǎn),將對網(wǎng)絡(luò)的性能和安全構(gòu)成重大威脅�？紤]上述惡劣環(huán)境,為了提高節(jié)點(diǎn)的傳輸成功率、減少傳輸延遲、提高網(wǎng)絡(luò)的安全性,本文提出了一種基于灰色關(guān)聯(lián)和協(xié)同計算的數(shù)據(jù)轉(zhuǎn)發(fā)信任模型GCTM(Gray correlation and Collaborative computing Trust Model)。相關(guān)的工作主要有以下兩個方面:第一,本文針對復(fù)雜環(huán)境下的機(jī)會網(wǎng)絡(luò)特性,提出了基于灰色關(guān)聯(lián)分析的直接信任值的計算。此模塊通過分析節(jié)點(diǎn)的移動特性以及普通節(jié)點(diǎn)與自私、惡意節(jié)點(diǎn)間的差異,選取接觸親密度、投遞可信度和位置親密度作為模型的多維信任參數(shù),計算節(jié)點(diǎn)的直接信任值,并采用灰色關(guān)聯(lián)算法調(diào)節(jié)直接信任值的權(quán)重系數(shù)。此舉將靜態(tài)的權(quán)重系數(shù)改進(jìn)為可根據(jù)信任參數(shù)間的內(nèi)在聯(lián)系自動調(diào)節(jié)各信任參數(shù)的動態(tài)權(quán)重系數(shù),該方法提高了機(jī)會網(wǎng)絡(luò)信任模型直接信任值計算的精確度。第二,本文針對機(jī)會網(wǎng)絡(luò)中自私與惡意節(jié)點(diǎn)的攻擊特性以及機(jī)會網(wǎng)絡(luò)中節(jié)點(diǎn)的稀疏性,提出了一種基于協(xié)同計算的過濾算法來計算推薦信任值。該算法利用數(shù)據(jù)發(fā)送節(jié)點(diǎn)與鄰居節(jié)點(diǎn)合作進(jìn)行協(xié)同計算。采用接觸親密度、投遞可信度作為計算推薦信任的參數(shù)。推薦信任模塊運(yùn)行時,評價節(jié)點(diǎn)向其周圍鄰居節(jié)點(diǎn)發(fā)送協(xié)同計算請求,鄰居節(jié)點(diǎn)收到請求后將推薦信息發(fā)送給評價節(jié)點(diǎn),考慮到網(wǎng)絡(luò)中存在部分自私與惡意節(jié)點(diǎn),所以需對推薦信息進(jìn)行過濾。本文中采用KNN算法對推薦信息進(jìn)行過濾處理。KNN算法可有效識別自私節(jié)點(diǎn)和惡意節(jié)點(diǎn)所發(fā)送的虛假推薦信任,并進(jìn)行濾除,該方法減輕了自私與惡意節(jié)點(diǎn)對機(jī)會網(wǎng)絡(luò)的干擾。采用協(xié)同計算技術(shù),多節(jié)點(diǎn)聯(lián)合計算推薦信任值,使計算結(jié)果更具可靠性。為了檢驗(yàn)GCTM信任值計算模型的有效性,本文使用ONE仿真器進(jìn)行仿真實(shí)驗(yàn)。實(shí)驗(yàn)中,通過在機(jī)會網(wǎng)絡(luò)中設(shè)置不同的自私與惡意節(jié)點(diǎn)個數(shù)來驗(yàn)證模型的安全性。實(shí)驗(yàn)結(jié)果表明,本文提出的GCTM模型下的數(shù)據(jù)轉(zhuǎn)發(fā)算法與經(jīng)典的Epidemic、MaxProp、Direct Delivery算法以及MDT信任模型相比,其傳輸成功率、傳輸延遲、路由開銷具有較好的改進(jìn)。
[Abstract]:Opportunistic network is an ad hoc network which does not need a complete path between the source node and the destination node and uses the encounter opportunity brought by the node movement to realize the network communication.However, if there are selfish or malicious nodes in opportunistic networks, they will pose a serious threat to the performance and security of the networks.Considering the bad environment mentioned above, in order to improve the transmission success rate, reduce the transmission delay and improve the security of the network, this paper presents a data forwarding trust model GCTM(Gray correlation and Collaborative computing Trust model based on gray correlation and cooperative computing.The main work of this paper is as follows: first, aiming at the characteristics of opportunistic networks in complex environments, this paper presents the calculation of direct trust values based on grey correlation analysis.By analyzing the mobility characteristics of nodes and the differences between common nodes and selfish and malicious nodes, this module selects contact affinity, delivery credibility and location affinity as the multidimensional trust parameters of the model, and calculates the direct trust value of the nodes.Grey correlation algorithm is used to adjust the weight coefficient of direct trust value.In this way, the static weight coefficient is improved to automatically adjust the dynamic weight coefficients of each trust parameter according to the inherent relationship between trust parameters. This method improves the accuracy of direct trust value calculation in the trust model of opportunistic network.Secondly, aiming at the attacks of selfish and malicious nodes in opportunistic networks and the sparsity of nodes in opportunistic networks, a filtering algorithm based on cooperative computing is proposed to calculate the recommended trust values.The algorithm uses the data sending node to cooperate with the neighbor node for collaborative computation.Contact affinity and delivery reliability are used as parameters to calculate recommendation trust.When the recommendation trust module is running, the evaluation node sends the cooperative computing request to the neighboring node, and the neighbor node sends the recommendation information to the evaluation node after receiving the request, considering that there are some selfish and malicious nodes in the network.So it is necessary to filter the recommendation information.In this paper, the KNN algorithm is used to filter the recommendation information. KNN algorithm can effectively identify the false recommendation trust sent by the selfish node and the malicious node, and filter the false recommendation trust. This method reduces the interference of the selfish and malicious nodes to the opportunity network.The cooperative computing technique is used to calculate the recommended trust value jointly, which makes the calculation results more reliable.In order to verify the validity of the GCTM trust value calculation model, this paper uses the ONE simulator to carry on the simulation experiment.In the experiment, the security of the model is verified by setting different numbers of selfish and malicious nodes in the opportunistic network.Experimental results show that compared with the classical Epidemicus MaxPropDirect Delivery algorithm and the MDT trust model, the proposed data forwarding algorithm based on the GCTM model has a better improvement on the transmission success rate, transmission delay and routing overhead than that of the classical Epidemicus MaxPropDirect Delivery algorithm and the MDT trust model.
【學(xué)位授予單位】：湖南科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP393.08

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