本文選題：Web服務(wù) + 服務(wù)選擇��； 參考：《遼寧大學(xué)》2014年碩士論文

【摘要】：Web服務(wù)作為面向服務(wù)體系架構(gòu)(Service-Oriented Architecture, SOA)的一種實(shí)現(xiàn)，快速地應(yīng)用于互聯(lián)網(wǎng)體系結(jié)構(gòu)中。然而數(shù)量激增的Web服務(wù)使得如何為用戶選擇滿足需求的服務(wù)成為了棘手的問(wèn)題。目前，針對(duì)服務(wù)選擇問(wèn)題的研究大多使用簡(jiǎn)單量化參數(shù)的方法評(píng)估服務(wù)信譽(yù)度，并以此為依據(jù)選擇和發(fā)現(xiàn)服務(wù)。然而由于存在異常用戶評(píng)價(jià)攻擊和虛假Q(mào)oS聲明，服務(wù)信譽(yù)度評(píng)估不夠真實(shí)準(zhǔn)確，因此建立一個(gè)全面描述服務(wù)信譽(yù)度的模型非常重要。 云模型能夠全面的描述模糊概念，并能多角度反映實(shí)體的特點(diǎn)。云模型根據(jù)樣本云滴的分布范圍和規(guī)律，用云表達(dá)定性概念的特征，并通過(guò)云的參數(shù)Ex、En和He反映實(shí)體的真實(shí)水平和穩(wěn)定程度，因而可以檢測(cè)出異常用戶的攻擊和虛假的服務(wù)信息。本文將云模型運(yùn)用到服務(wù)信譽(yù)度計(jì)算過(guò)程中，該模型可以全面的保留計(jì)算過(guò)程中參數(shù)的信息，避免單一數(shù)值表達(dá)的局限性，同時(shí)減小虛假評(píng)價(jià)和虛假Q(mào)oS聲明對(duì)計(jì)算服務(wù)信譽(yù)度的影響，使估更加準(zhǔn)確。具體工作如下： 首先，，建立用戶評(píng)價(jià)主觀服務(wù)信譽(yù)度評(píng)估模型。對(duì)云模型的輸入樣本進(jìn)行分析，提出基于PeerTrust模型和用戶歷史評(píng)價(jià)信息的評(píng)價(jià)相似度算法，相似度作為云滴輸入后，輸出用戶評(píng)價(jià)質(zhì)量云，反映出用戶評(píng)價(jià)的真實(shí)性和穩(wěn)定性特征，剔除評(píng)價(jià)質(zhì)量低于閾值的評(píng)價(jià)、懲罰行為波動(dòng)的用戶，使得服務(wù)信譽(yù)度計(jì)算結(jié)果更加真實(shí)準(zhǔn)確，有效地抗擊異常用戶的虛假評(píng)價(jià)攻擊。 其次，建立服務(wù)提供者客觀服務(wù)信譽(yù)度評(píng)估模型，即評(píng)估QoS聲明的可靠性。統(tǒng)計(jì)云模型的輸入云滴樣本，提出關(guān)于QoS聲明與實(shí)際值之間的相似度算法，并通過(guò)云模型輸出服務(wù)的聲明質(zhì)量云，反映出QoS聲明的真實(shí)性和穩(wěn)定性，對(duì)于發(fā)布虛假Q(mào)oS聲明的服務(wù)，其信譽(yù)度將受到懲罰而降低。并將綜合服務(wù)信譽(yù)度作為服務(wù)選擇的依據(jù)。 最后，將本文提出的基于云模型的服務(wù)信譽(yù)度計(jì)算方法與其他方法進(jìn)行對(duì)比分析，在仿真實(shí)驗(yàn)環(huán)境中模擬了多個(gè)場(chǎng)景進(jìn)行測(cè)試，結(jié)果表明，本文提出的方法在抗擊虛假信息、有效識(shí)別波動(dòng)行為和交易成功率上都有較好的表現(xiàn)，驗(yàn)證了本文提出方法的有效性。
[Abstract]:As an implementation of Service-Oriented Architecture (SOA), Web services are rapidly applied to Internet architecture. However, the proliferation of Web services makes it difficult for users to choose services that meet their needs. At present, most of the researches on service selection use simple quantitative parameters to evaluate the service reputation, and select and discover the service based on it. However, due to the existence of abnormal user evaluation attacks and false QoS statements, the evaluation of service reputation is not true and accurate, so it is very important to establish a comprehensive model to describe the service reputation. The cloud model can describe the fuzzy concept comprehensively, and can reflect the characteristics of the entity from many angles. According to the distribution range and law of sample cloud droplets, the cloud model expresses the characteristics of qualitative concept by cloud, and reflects the real level and stability of entity through the parameters of cloud E _ (n) and he. Therefore, abnormal user attacks and false service information can be detected. In this paper, the cloud model is applied to the service reputation calculation process. The model can keep the information of the parameters in the calculation process and avoid the limitation of the single numerical expression. At the same time, the influence of false evaluation and false QoS statement on the reputation of computing services is reduced, so that the estimation is more accurate. The specific work is as follows: First of all, the subjective service reputation evaluation model of user evaluation is established. After analyzing the input samples of cloud model, an evaluation similarity algorithm based on PeerTrust model and user history evaluation information is proposed. It reflects the authenticity and stability of the user evaluation, removes the evaluation of the evaluation quality below the threshold value, punishes the users whose behavior fluctuates, makes the calculation results of the service reputation more true and accurate, and effectively resists the false evaluation attack of the abnormal users. Secondly, an objective service reputation evaluation model for service providers is established to evaluate the reliability of QoS claims. The input cloud drop samples of the cloud model are counted, and the similarity algorithm between the QoS declaration and the actual value is proposed, and the declaration quality cloud of the service is outputted by the cloud model, which reflects the authenticity and stability of the QoS declaration. For services that issue false QoS claims, their reputation will be punished. And the comprehensive service reputation as the basis for service selection. Finally, this paper compares the cloud model-based service reputation calculation method with other methods, and simulates several scenarios to test in the simulation experimental environment. The results show that the method proposed in this paper is fighting against false information. It is proved that the proposed method is effective in identifying volatility behavior and trading success rate.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.09

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