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  本文選題：云模型 + 正向云算法��； 參考：《西安電子科技大學》2014年碩士論文

【摘要】：主觀信任管理技術在解決動態(tài)網(wǎng)絡中數(shù)據(jù)可靠傳輸問題以及分布式網(wǎng)絡應用中用戶交易的可信性問題等方面具有很高的靈活性和有效性，已成為了國內(nèi)外的研究熱點�，F(xiàn)有的主觀信任管理模型都無法兼顧“信任”這一概念的的模糊性、主觀性及隨機性等特點，很難準確的完成信任的定性、定量之間的轉換。云模型是定性定量概念之間實現(xiàn)有效的相互轉換的數(shù)學模型，能夠同時反應出概念的不確定性、模糊性及隨機性，，在處理認知概念的不確定性方面具有明顯優(yōu)勢。然而云模型中，廣泛應用于信任管理中的逆向云算法的穩(wěn)定性較差。同時，當前的正向云算法依然存在些同人類認知相矛盾的圖形特征，利用正向云算法生成的點，即云滴也不可避免的存在誤差，這將直接影響云模型應用時的準確性。本文針對基于云模型理論的主觀信任管理模型進行了較為深入的研究。 首先，本文分析了正向云算法中所存在的問題，并對其進行了修正。實現(xiàn)了對不同論域值所對應的認知歧義的限制，并提出了計算各點處不同認知歧義值的公式。對修正后的正向云算法進行仿真和分析，結果說明修正算法更能夠滿足人類的認知，能夠更準確的完成對定性概念的刻畫。 然后，基于云模型中正向云修正算法，本文設計了一種新的基于條件云的信任度量模型。新的模型避免了逆向云算法的使用，大大降低了基于云模型的信任管理模型需要收集的數(shù)據(jù)量。仿真結果證明了新的信任度量模型的正確性，并將其應用在Ad hoc網(wǎng)絡中的可信傳輸服務中，驗證了新算法的有效性。 最后，在信任度量模型的基礎上，本文設計了信任推薦系統(tǒng)和歷史系統(tǒng)。歷史系統(tǒng)中，模擬了歷史信任信息對當前信任值的影響，提出了“機器時間”的概念，是用于衡量網(wǎng)絡世界不同于現(xiàn)實世界的時間單位；推薦系統(tǒng)中的信任推薦算法能夠在確保推薦意義的前提下從內(nèi)部抵抗共謀攻擊。通過這兩個系統(tǒng)的設計，本文最終實現(xiàn)了一個基于條件云的主觀信任管理模型。
[Abstract]:Subjective trust management technology has high flexibility and effectiveness in solving the problem of reliable data transmission in dynamic networks and the credibility of user transactions in distributed network applications. It has become a research hotspot at home and abroad. The existing subjective trust management models can not take into account the fuzziness, subjectivity and randomness of the concept of "trust", so it is difficult to complete the transformation between qualitative and quantitative trust accurately. Cloud model is a mathematical model which can realize the effective transformation between qualitative and quantitative concepts. It can reflect the uncertainty, fuzziness and randomness of the concept at the same time. It has obvious advantages in dealing with the uncertainty of cognitive concepts. However, the reverse cloud algorithm, which is widely used in trust management, has poor stability in cloud model. At the same time, the current forward cloud algorithm still has some graphic features that are inconsistent with human cognition. The point generated by the forward cloud algorithm, that is, cloud droplets, will inevitably have errors, which will directly affect the accuracy of cloud model application. In this paper, the subjective trust management model based on cloud model theory is studied deeply. Firstly, the problems in the forward cloud algorithm are analyzed and corrected. The limits of cognitive ambiguity corresponding to different domain values are realized, and the formulas for calculating different cognitive ambiguity values at different points are proposed. The simulation and analysis of the modified forward cloud algorithm show that the modified algorithm can satisfy the human cognition more accurately and finish the characterization of qualitative concepts more accurately. Then, based on the forward cloud correction algorithm in the cloud model, this paper designs a new trust quantity model based on conditional cloud. The new model avoids the use of the reverse cloud algorithm and greatly reduces the amount of data to be collected by the trust management model based on the cloud model. The simulation results show that the new model is correct, and it is applied to the trusted transport service in Ad hoc network, and the validity of the new algorithm is verified. Finally, trust recommendation system and history system are designed on the basis of trust degree model. In the historical system, the influence of the historical trust information on the current trust value is simulated, and the concept of "machine time" is proposed, which is used to measure the difference between the network world and the real world. The trust recommendation algorithm in the recommendation system can resist collusion attack from the inside on the premise of ensuring the recommendation meaning. Through the design of these two systems, a subjective trust management model based on conditional cloud is implemented.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.08

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本文編號：1953231