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  本文選題：協(xié)同過濾推薦　切入點(diǎn)：攻擊概貌檢測　出處：《燕山大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：協(xié)同過濾推薦技術(shù)作為應(yīng)用最廣的個性化推薦技術(shù)之一，被認(rèn)為是解決信息爆炸時代信息過載問題的有效方法。但是由于協(xié)同過濾推薦系統(tǒng)的開放性和用戶參與性，系統(tǒng)存在嚴(yán)重的安全隱患。一些惡意用戶出于商業(yè)的目的，通過向系統(tǒng)中注入大量的虛假用戶概貌來使得推薦系統(tǒng)產(chǎn)生有利于他們自己的推薦結(jié)果。因此，如何保障協(xié)同過濾推薦系統(tǒng)的安全成為了協(xié)同過濾推薦技術(shù)研究中的一個熱點(diǎn)。本文在對國內(nèi)外研究現(xiàn)狀深入分析的基礎(chǔ)上，進(jìn)一步對協(xié)同過濾推薦系統(tǒng)的攻擊特征提取進(jìn)行了研究。 針對已有的攻擊特征種類還不夠豐富，，攻擊檢測能力不強(qiáng)的問題，從豐富攻擊特征的角度上。首先，在深入分析攻擊概貌特征的基礎(chǔ)上，基于傳統(tǒng)的攻擊檢測特征只關(guān)注攻擊評分值的分布特征，忽視了攻擊概貌在選擇填充項目時隨機(jī)選擇的這個特點(diǎn)，提出了攻擊概貌的關(guān)聯(lián)規(guī)則特征。從關(guān)聯(lián)規(guī)則挖掘的角度，找到項目中存在的強(qiáng)關(guān)聯(lián)規(guī)則，利用攻擊用戶比正常用戶滿足關(guān)聯(lián)規(guī)則的概率低這個特點(diǎn)，來對攻擊概貌進(jìn)行檢測，提高了對攻擊用戶的檢測能力。其次，攻擊概貌和正常用戶概貌在評分分布上的區(qū)別使得攻擊概貌的目標(biāo)項目在受攻擊前后評分分布有著巨大的變化，而已有的攻擊特征沒能很好的反映攻擊概貌的這個特點(diǎn)。針對這個問題，提出攻擊概貌的目標(biāo)項目特征，能夠描述項目在受攻擊前后平均評分值的變化，選擇其中大于一個閾值的項目作為攻擊概貌的目標(biāo)項目。本文的研究能夠有效的豐富攻擊特征，提高攻擊概貌檢測能力。 最后，對本文提出的兩種攻擊特征進(jìn)行了實(shí)驗驗證與分析，驗證其有效性，并且對今后的研究工作進(jìn)行了展望。
[Abstract]:As one of the most widely used personalized recommendation technologies, collaborative filtering recommendation technology is considered to be an effective method to solve the problem of information overload in the era of information explosion, but because of the openness and user participation of collaborative filtering recommendation system. There are serious security risks in the system. For commercial purposes, some malicious users make the recommendation system produce recommendation results in their own interests by injecting a large number of false user profiles into the system. How to ensure the security of collaborative filtering recommendation system has become a hot spot in the research of collaborative filtering recommendation technology. Furthermore, the attack feature extraction of collaborative filtering recommendation system is studied. In view of the problem that the existing attack features are not rich enough and the ability of attack detection is not strong, from the point of view of enriching attack features, firstly, on the basis of in-depth analysis of attack general features, Based on the fact that the traditional attack detection features only focus on the distribution of attack score, and neglects the random selection of attack profile when selecting filling items, the association rule feature of attack profile is proposed. Find the strong association rules existing in the project, take advantage of the fact that the probability of attacking users meeting the association rules is lower than that of normal users, to detect the attack profile, and improve the detection ability of attack users. Secondly, The difference between the attack profile and the normal user profile results in a huge change in the score distribution of the target item before and after the attack. However, the existing attack features can not well reflect the characteristics of the attack profile. In view of this problem, the target item feature of the attack profile can describe the change of the average score of the item before and after the attack. The research in this paper can effectively enrich the attack characteristics and improve the ability of attack profile detection. Finally, the two attack features proposed in this paper are tested and analyzed to verify their effectiveness, and the future research work is prospected.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.3;TP393.08

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