【摘要】：伴隨互聯(lián)網(wǎng)的普及與電子商務(wù)的快速發(fā)展,信息數(shù)據(jù)量以指數(shù)級別增長的同時(shí)帶來了“信息過載”問題。推薦算法通過數(shù)據(jù)挖掘、機(jī)器學(xué)習(xí)等方式挖掘海量信息中能夠幫助電子商務(wù)網(wǎng)站為其客戶提供符合個(gè)性化需求的決策支撐和信息服務(wù),一定程度上有效的緩解了海量數(shù)據(jù)問題。但系統(tǒng)自身的公開性、推薦算法本身存在的設(shè)計(jì)缺陷以及用戶的介入性導(dǎo)致系統(tǒng)容易遭受惡意干擾、蓄意攻擊等操縱行為。因此,安全性成為推薦系統(tǒng)的關(guān)鍵問題。通常將有目的去偽造、更改評分?jǐn)?shù)據(jù)的惡意操作稱為用戶概貌注入攻擊或者托攻擊。傳統(tǒng)的協(xié)同過濾技術(shù)已然無法滿足推薦系統(tǒng)對高安全性、防御性、準(zhǔn)確性等推薦可靠性要求。部分商家向推薦系統(tǒng)中惡意注入攻擊用戶概貌,對推薦系統(tǒng)結(jié)果進(jìn)行人為干預(yù)企圖謀取私利。這些惡意操作行為嚴(yán)重危害了推薦系統(tǒng)的安全性。如何檢測出托攻擊并采取有效的方法來防御托攻擊刻不容緩,已成為該領(lǐng)域?qū)＜覍W(xué)者重要研究問題。 相似度度量是協(xié)同過濾算法的核心模塊,但易于遭受推薦攻擊問題。近年來,信譽(yù)模型被融合到推薦流程中,加強(qiáng)協(xié)同過濾算法的魯棒性和推薦精確性�；谀壳把芯口厔�,本文提出了兩種改進(jìn)方法提高推薦系統(tǒng)的防御能力。本文主要?jiǎng)?chuàng)新改進(jìn)內(nèi)容如下： (1)基于信息熵相似度的托攻擊防御方法 在協(xié)同過濾相關(guān)理論的基礎(chǔ)上,針對當(dāng)前相似度度量方法僅考慮評分矩陣數(shù)據(jù)的局限性,本文提出信息熵來度量正常用戶與惡意用戶間評分變化幅度差異。融合信息熵模型作為度量相似度的影響因子,彌補(bǔ)了系統(tǒng)遭受攻擊時(shí)僅依靠傳統(tǒng)相似度不足以區(qū)分惡意用戶的缺陷性。在皮爾森相關(guān)系數(shù)基礎(chǔ)上,本文提出一種改進(jìn)的相似度度量方法(E-CF),結(jié)合評分變化幅度差異降低注入用戶概貌的相似性。實(shí)驗(yàn)結(jié)果表明,E-CF客觀地反映托攻擊情況下系統(tǒng)防御性增強(qiáng),并提高了算法精確性。 (2)融合信任更新機(jī)制的防攻擊推薦算法研究 隨著社交網(wǎng)絡(luò)研究的飛速發(fā)展,信任關(guān)系網(wǎng)絡(luò)被廣泛應(yīng)用到個(gè)性化推薦算法研究中�？紤]到推薦用戶在過去的推薦歷史中所起到的作用也是一個(gè)重要的推薦依據(jù)因素,即推薦用戶的信任度,引入信任更新機(jī)制。通過融合信任度和相似度,建立復(fù)合推薦權(quán)重模型(TE-CF),以真實(shí)評分反饋為手段動(dòng)態(tài)更新復(fù)合權(quán)重,降低攻擊用戶概貌對推薦結(jié)果的影響。實(shí)驗(yàn)結(jié)果表明,TE-CF客觀地反映攻擊情況下系統(tǒng)防御性增強(qiáng),并提高了算法精確性。 基于目前研究現(xiàn)狀,本文提出兩種托攻擊防御解決方案。最后,與現(xiàn)有算法進(jìn)行實(shí)驗(yàn)驗(yàn)證和對比分析,并提出進(jìn)一步研究內(nèi)容。
[Abstract]:With the popularization of Internet and the rapid development of E-commerce, the data amount of information increases exponentially and brings about the problem of "information overload" at the same time. By means of data mining and machine learning, the recommendation algorithm can help e-commerce website to provide decision support and information service to its customers, which can alleviate the problem of mass data to a certain extent. However, the openness of the system itself, the design defects of the recommended algorithm itself and the user's involvement result in the system being vulnerable to malicious interference, deliberate attack and other manipulation behaviors. Therefore, security has become the key issue of recommendation system. The malicious action to change the score data is called user profile injection attack or proxy attack. The traditional collaborative filtering technology has been unable to meet the recommendation system for high security, defensibility, accuracy and other recommended reliability requirements. Some merchants inject malicious user profile into the recommendation system and interfere with the result of the recommendation system in an attempt to gain self-interest. These malicious operations seriously endanger the security of the recommendation system. How to detect the support attack and take effective methods to defend the support attack is urgent, which has become an important research issue of experts and scholars in this field. Similarity measurement is the core module of collaborative filtering algorithm, but it is vulnerable to recommendation attack. In recent years, reputation models have been incorporated into the recommendation process to enhance the robustness and accuracy of collaborative filtering algorithms. Based on the current research trend, two improved methods are proposed to improve the defense capability of the recommendation system. The main innovation and improvement contents of this paper are as follows: (1) based on the theory of collaborative filtering, the supporting attack defense method based on information entropy similarity is based on the theory of collaborative filtering, aiming at the limitation of the current similarity measurement method only considering the data of scoring matrix. In this paper, information entropy is proposed to measure the variation of scores between normal users and malicious users. The fusion information entropy model as an influential factor to measure similarity makes up for the defects of malicious users when the system is attacked by traditional similarity. Based on Pearson's correlation coefficient, an improved similarity measure method (E-CF) is proposed in this paper, which reduces the similarity of injected user profile combined with the variation of scoring range. The experimental results show that E-CF objectively reflects the system defensibility and improves the accuracy of the algorithm under the condition of support attack. (2) Research on Anti-attack recommendation algorithm based on Trust Renewal Mechanism; with the rapid development of social network research, trust relation network has been widely used in personalized recommendation algorithm research. Considering the role of the recommended user in the past recommendation history is also an important recommendation basis, that is, the degree of trust of the recommended user, the introduction of trust update mechanism. A composite recommendation weight model (TE-CF) is established by combining trust degree and similarity degree. Real score feedback is used as a means to dynamically update composite weights to reduce the impact of attacking user profile on recommendation results. The experimental results show that TE-CF objectively reflects the system defensiveness and improves the accuracy of the algorithm. Based on the current research situation, this paper proposes two solutions for supporting attack defense. Finally, experimental verification and comparative analysis with the existing algorithms are carried out, and further research contents are put forward.
【學(xué)位授予單位】：廣東工業(yè)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

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 張婷;曾慶鵬;高勝保;肖異瑤;;基于時(shí)域背離特征分析的托攻擊檢測算法[J];南昌大學(xué)學(xué)報(bào)(工科版);2017年01期

相關(guān)碩士學(xué)位論文 前1條

1 張順;基于用戶重要性的協(xié)同推薦算法研究[D];安徽大學(xué);2016年

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本文編號：2431672