發(fā)布時(shí)間：2018-01-26 09:49

  本文關(guān)鍵詞： 協(xié)同過(guò)濾 攻擊檢測(cè) AP聚類(lèi) 用戶(hù)概貌 概貌特征屬性　出處：《電子科技大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：電子商務(wù)的迅速發(fā)展給人們的生活提供了更加豐富的選擇,但也使得服務(wù)信息呈現(xiàn)“超載”趨勢(shì),推薦系統(tǒng)是過(guò)濾信息的重要手段,是解決信息超載卓有成效的方法。然而由于系統(tǒng)本身對(duì)用戶(hù)的開(kāi)放性及靈敏性,使其很容易遭到外界的攻擊。部分惡意商家在商業(yè)利益的驅(qū)動(dòng)下,刻意地向系統(tǒng)中植入一些偽造的用戶(hù)概貌來(lái)影響推薦系統(tǒng)的準(zhǔn)確性。如何對(duì)外界攻擊進(jìn)行防御和檢測(cè),確保電子商務(wù)推薦系統(tǒng)的安全成為近年來(lái)信息推薦領(lǐng)域的一個(gè)新的研究熱點(diǎn)。本文綜合分析了國(guó)內(nèi)外有關(guān)推薦系統(tǒng)安全性的研究現(xiàn)狀,并針對(duì)基于協(xié)同過(guò)濾的攻擊檢測(cè)算法進(jìn)行了深入研究,主要研究工作如下:1.深入分析了協(xié)同過(guò)濾算法的基本思想和工作流程;研究推薦攻擊的相關(guān)問(wèn)題,理解推薦攻擊的策略;根據(jù)攻擊用戶(hù)概貌的評(píng)分策略對(duì)攻擊模型進(jìn)行了分類(lèi)。將現(xiàn)有經(jīng)典的攻擊檢測(cè)算法進(jìn)行了分類(lèi),通過(guò)實(shí)驗(yàn)根據(jù)幾種標(biāo)準(zhǔn)的攻擊模型生成對(duì)應(yīng)的攻擊用戶(hù)概貌植入至原始系統(tǒng),分析比較了攻擊前后不同攻擊比例和填充比例對(duì)推薦系統(tǒng)平均預(yù)測(cè)偏離度和命中率的影響情況。2.理解研究基于Hv-score值的UnRAP無(wú)監(jiān)督攻擊檢測(cè)算法,分析算法的基本思想和實(shí)現(xiàn)流程。在UnRAP檢測(cè)算法的基礎(chǔ)上,事先對(duì)系統(tǒng)中的所有用戶(hù)進(jìn)行聚類(lèi),并將類(lèi)中的用戶(hù)評(píng)分進(jìn)行壓縮。針對(duì)群體用戶(hù)而不是單個(gè)用戶(hù)來(lái)對(duì)UnRAP算法進(jìn)行改進(jìn),得到一種基于UnRAP的群組攻擊檢測(cè)算法AP-UnRAP。改進(jìn)后的算法充分考慮了攻擊用戶(hù)內(nèi)部之間的高相似性,尋找目標(biāo)項(xiàng)目時(shí)相對(duì)單個(gè)用戶(hù)概貌更加準(zhǔn)確。3.結(jié)合用戶(hù)概貌特征屬性,提出一種基于AP聚類(lèi)的混合無(wú)監(jiān)督攻擊檢測(cè)算法AP-Mix。通過(guò)將用戶(hù)原始評(píng)分矩陣采用PCA降維,并將主分量信息和用戶(hù)概貌特征屬性進(jìn)行維度組合,用來(lái)表示每個(gè)用戶(hù)的整體評(píng)分行為;接著,利用一種自適應(yīng)AP聚類(lèi)算法對(duì)系統(tǒng)中的所有用戶(hù)進(jìn)行群組劃分;最后,計(jì)算每個(gè)群組的平均評(píng)分偏離度(GRDMA)來(lái)找到攻擊用戶(hù)所在的某個(gè)群組,進(jìn)而檢測(cè)出植入的攻擊用戶(hù)。AP-Mix用組合后的信息代表用戶(hù)的完整行為,加大了攻擊用戶(hù)和正常用戶(hù)的區(qū)分度,用戶(hù)群體劃分的效果更好,檢測(cè)性能越強(qiáng);且事先不需要知道任何攻擊的知識(shí),真正做到了無(wú)監(jiān)督檢測(cè)。最后,通過(guò)實(shí)驗(yàn)與現(xiàn)有經(jīng)典檢測(cè)算法進(jìn)行對(duì)比來(lái)驗(yàn)證本文提出新算法的檢測(cè)高效性。
[Abstract]:The rapid development of electronic commerce provides more choices for people's life, but also makes service information "overload" trend, recommendation system is an important means of filtering information. It is a very effective way to solve the problem of information overload. However, because of the openness and sensitivity of the system to users, it is easy to be attacked by the outside world. Some malicious businesses are driven by commercial interests. Deliberately implant some fake user profiles into the system to affect the accuracy of the recommendation system. How to defend against and detect external attacks. To ensure the security of E-commerce recommendation system has become a new research hotspot in the field of information recommendation in recent years. And the attack detection algorithm based on collaborative filtering is deeply studied. The main research work is as follows: 1. The basic idea and workflow of collaborative filtering algorithm are deeply analyzed. To study the related problems of recommendation attack and understand the strategy of recommendation attack; The attack models are classified according to the scoring strategy of the attack user profile, and the existing classic attack detection algorithms are classified. According to several standard attack models, the corresponding attack user profile is generated by experiments and implanted into the original system. This paper analyzes and compares the influence of different attack ratio and filling ratio before and after attack on the average predictive deviation and hit rate of recommendation system. 2. Understand and study UnRAP unsupervised attack based on Hv-score value. Detection algorithm. The basic idea and implementation flow of the algorithm are analyzed. Based on the UnRAP detection algorithm, all users in the system are clustered in advance. The UnRAP algorithm is improved by compressing the user score in the class and aiming at the group users rather than the individual users. An AP-UnRAP-based group attack detection algorithm based on UnRAP is proposed. The improved algorithm takes into account the high similarity among the users. When looking for the target item, it is more accurate than a single user. 3. Combine the feature attribute of user profile. An AP-Mix-based hybrid unsupervised attack detection algorithm based on AP clustering is proposed. The user's original score matrix is reduced by PCA. The principal component information and the feature attribute of user profile are combined to represent the overall rating behavior of each user. Then, an adaptive AP clustering algorithm is used to group all users in the system. Finally, the average score deviation of each group is calculated to find the group in which the user is attacked. Furthermore, the embedded attack user. AP-Mix uses the combined information to represent the complete behavior of the user, which increases the degree of discrimination between the attacking user and the normal user, and the effect of user group division is better. The stronger the detection performance is; And we do not need to know any knowledge of attack in advance to achieve unsupervised detection. Finally, the effectiveness of the new algorithm is verified by comparing the experimental results with the existing classical detection algorithms.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP391.3;TP393.08

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