推薦系統(tǒng)攻擊檢測算法的研究
發(fā)布時間:2018-01-26 09:49
本文關(guān)鍵詞: 協(xié)同過濾 攻擊檢測 AP聚類 用戶概貌 概貌特征屬性 出處:《電子科技大學》2014年碩士論文 論文類型:學位論文
【摘要】:電子商務的迅速發(fā)展給人們的生活提供了更加豐富的選擇,但也使得服務信息呈現(xiàn)“超載”趨勢,推薦系統(tǒng)是過濾信息的重要手段,是解決信息超載卓有成效的方法。然而由于系統(tǒng)本身對用戶的開放性及靈敏性,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)動下,刻意地向系統(tǒng)中植入一些偽造的用戶概貌來影響推薦系統(tǒng)的準確性。如何對外界攻擊進行防御和檢測,確保電子商務推薦系統(tǒng)的安全成為近年來信息推薦領(lǐng)域的一個新的研究熱點。本文綜合分析了國內(nèi)外有關(guān)推薦系統(tǒng)安全性的研究現(xiàn)狀,并針對基于協(xié)同過濾的攻擊檢測算法進行了深入研究,主要研究工作如下:1.深入分析了協(xié)同過濾算法的基本思想和工作流程;研究推薦攻擊的相關(guān)問題,理解推薦攻擊的策略;根據(jù)攻擊用戶概貌的評分策略對攻擊模型進行了分類。將現(xiàn)有經(jīng)典的攻擊檢測算法進行了分類,通過實驗根據(jù)幾種標準的攻擊模型生成對應的攻擊用戶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推薦系統(tǒng)平均預測偏離度和命中率的影響情況。2.理解研究基于Hv-score值的UnRAP無監(jiān)督攻擊檢測算法,分析算法的基本思想和實現(xiàn)流程。在UnRAP檢測算法的基礎上,事先對系統(tǒng)中的所有用戶進行聚類,并將類中的用戶評分進行壓縮。針對群體用戶而不是單個用戶來對UnRAP算法進行改進,得到一種基于UnRAP的群組攻擊檢測算法AP-UnRAP。改進后的算法充分考慮了攻擊用戶內(nèi)部之間的高相似性,尋找目標項目時相對單個用戶概貌更加準確。3.結(jié)合用戶概貌特征屬性,提出一種基于AP聚類的混合無監(jiān)督攻擊檢測算法AP-Mix。通過將用戶原始評分矩陣采用PCA降維,并將主分量信息和用戶概貌特征屬性進行維度組合,用來表示每個用戶的整體評分行為;接著,利用一種自適應AP聚類算法對系統(tǒng)中的所有用戶進行群組劃分;最后,計算每個群組的平均評分偏離度(GRDMA)來找到攻擊用戶所在的某個群組,進而檢測出植入的攻擊用戶。AP-Mix用組合后的信息代表用戶的完整行為,加大了攻擊用戶和正常用戶的區(qū)分度,用戶群體劃分的效果更好,檢測性能越強;且事先不需要知道任何攻擊的知識,真正做到了無監(jiān)督檢測。最后,通過實驗與現(xiàn)有經(jīng)典檢測算法進行對比來驗證本文提出新算法的檢測高效性。
[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.
【學位授予單位】:電子科技大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:TP391.3;TP393.08
【參考文獻】
相關(guān)期刊論文 前1條
1 張富國;徐升華;;推薦系統(tǒng)安全問題及技術(shù)研究綜述[J];計算機應用研究;2008年03期
,本文編號:1465306
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