本文選題：對等網(wǎng)絡(luò) + 可信度��； 參考：《南京航空航天大學(xué)》2016年碩士論文

【摘要】：區(qū)別于傳統(tǒng)的客戶端/服務(wù)器(Client/Server)模式,P2P(Peer-to-Peer)網(wǎng)絡(luò)堅持“人人為我,我為人人”的原則,具有動態(tài)性、高度共享性、公平性等特點,帶給人們?nèi)碌木W(wǎng)絡(luò)共享體驗。與此同時,大量匿名節(jié)點的隨意進出,使網(wǎng)絡(luò)容易受到不同類型惡意用戶的攻擊,其中共謀團體對網(wǎng)絡(luò)秩序的破壞更為嚴重,還需承受理性用戶自私行為的影響。因此,為了減少以上安全風(fēng)險對網(wǎng)絡(luò)的危害,建立一個有效、合理和可靠的信任模型是及其重要的。本文建立了基于節(jié)點偏好相似度的混合推薦信任模型和基于聚類與激勵機制的混合推薦信任模型,具體研究工作與創(chuàng)新如下:(1)提出了一個基于節(jié)點偏好相似度的混合推薦信任模型(PSRTrust)P2P網(wǎng)絡(luò)中大量新進節(jié)點的加入造成可信矩陣變得稀疏,導(dǎo)致依據(jù)可信矩陣迭代計算的節(jié)點的全局可信度不夠準確,使得交易成功率較低,針對此問題,提出相似隨機游走策略(Similarity Random Walk,SRW)預(yù)測缺省的可信數(shù)據(jù),提高交易成功率;已存在信任模型中的不合理假設(shè)造成了網(wǎng)絡(luò)中絕大多數(shù)交易發(fā)生在極少數(shù)具有較高可信度節(jié)點上,針對此問題,提出了多層次選擇策略擴大節(jié)點局限的選擇范圍,且在減少節(jié)點負載的同時增加了網(wǎng)絡(luò)資源利用率;針對網(wǎng)絡(luò)中可信數(shù)據(jù)分布式存儲的安全問題,提出了基于改進Chord協(xié)議的多信任管理者的可信數(shù)據(jù)管理機制,避免惡意節(jié)點隨意篡改可信數(shù)據(jù),并給出可信數(shù)據(jù)存儲計算的分布式算法;為防治共謀團體對網(wǎng)絡(luò)的嚴重危害,提出基于節(jié)點行為相似的聚類方法識別網(wǎng)絡(luò)中參與共謀團體的惡意節(jié)點,該方法較為簡單易行。(2)提出了一個基于聚類與激勵機制的混合推薦信任模型(IPSRTrust)改進了PSRTrust模型中基于節(jié)點單屬性行為相似的簡單聚類方法,提出了基于節(jié)點多屬性行為相似的蟻群聚類方法以提高共謀團體識別的準確率和穩(wěn)定性;運用基于節(jié)點雙層貢獻度和動態(tài)規(guī)劃的激勵機制以減少網(wǎng)絡(luò)中理性用戶的數(shù)量,穩(wěn)定網(wǎng)絡(luò)秩序。仿真實驗表明所提出的PSRTrust、IPSRTrust兩種模型的性能與經(jīng)典的信任模型EigenTrust、PowerTrust相比較,在可信數(shù)據(jù)稀疏情況下可使網(wǎng)絡(luò)保持較穩(wěn)定秩序,且對于共謀團體的遏制效果尤為突出,并且IPSRTrust中提出的激勵機制可有效減少網(wǎng)絡(luò)中自私理性用戶的數(shù)量。
[Abstract]:Different from the traditional client / Server (client / Server) model, the P2P Peer-to-Peer network adheres to the principle of "everyone for me, I for everyone", which has the characteristics of dynamic, high sharing and fairness, and brings people a new network sharing experience. At the same time, the random access of a large number of anonymous nodes makes the network vulnerable to attack by different types of malicious users, among which the collusion group has more serious damage to the network order and has to bear the influence of rational user selfishness. Therefore, it is very important to establish an effective, reasonable and reliable trust model in order to reduce the harm of the above security risks to the network. In this paper, a hybrid recommendation trust model based on node preference similarity and a hybrid recommendation trust model based on clustering and incentive mechanism are established. Specific research work and innovation are as follows: (1) A hybrid recommendation trust model based on node preference similarity is proposed. The addition of a large number of new nodes in PSRTrustN P2P network causes the trust matrix to become sparse. As a result, the global credibility of nodes calculated by trust matrix iteration is not accurate enough, and the transaction success rate is low. In view of this problem, a similar random walk strategy is proposed to predict the default trusted data to improve the transaction success rate. The unreasonable assumption in the existing trust model causes most of the transactions in the network to occur on a very small number of highly reliable nodes. In order to solve this problem, a multi-level selection strategy is proposed to expand the selection range of node limitation. In order to solve the security problem of distributed storage of trusted data in the network, a new trusted data management mechanism based on improved Chord protocol for multi-trust managers is proposed, which can reduce the load of nodes and increase the utilization of network resources. To prevent malicious nodes from tampering with trusted data at will, and to provide a distributed algorithm for computing trusted data storage, in order to prevent the serious harm of collusion groups to the network, A clustering method based on similarity of node behavior is proposed to identify malicious nodes participating in collusion groups in the network. A hybrid recommendation trust model based on clustering and incentive mechanism is proposed, which improves the simple clustering method based on the similarity of node single attribute behavior in PSRTrust model. An ant colony clustering method based on node multi-attribute behavior similarity is proposed to improve the accuracy and stability of collusion group identification, and to reduce the number of rational users in the network by using the incentive mechanism based on node two-level contribution and dynamic programming. Stable network order. The simulation results show that compared with the classical trust model EigenTrusti PowerTrust, the proposed two models can keep the network stable and orderly under the condition of sparse trusted data, and the containment effect of the two models is especially prominent for the collusion group, and the simulation results show that the proposed two models can keep the network stable and orderly under the condition of sparse trusted data. And the incentive mechanism proposed in IPSRTrust can effectively reduce the number of selfish and rational users in the network.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3
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本文編號：1913364