本文選題：社交網(wǎng)絡(luò) + 攻擊檢測(cè)�。� 參考：《燕山大學(xué)》2014年碩士論文

【摘要】：隨著社交網(wǎng)絡(luò)的迅猛發(fā)展，越來越多的用戶通過社交網(wǎng)絡(luò)溝通交流、分享信息，然而由于社交網(wǎng)絡(luò)的開放性，社交網(wǎng)絡(luò)用戶更容易受到安全威脅，尤其是Sybil攻擊呈上漲趨勢(shì)，一些惡意用戶為了謀求利益，創(chuàng)建大量的惡意身份，向社交網(wǎng)絡(luò)中真實(shí)用戶傳播惡意信息或者提升自己團(tuán)體的影響，嚴(yán)重威脅著社交網(wǎng)絡(luò)的安全。本文在綜合分析國內(nèi)外研究現(xiàn)狀的基礎(chǔ)上，針對(duì)如何在解決大規(guī)模社交網(wǎng)絡(luò)中Sybil攻擊檢測(cè)問題進(jìn)行了深入地研究。 首先，針對(duì)現(xiàn)有Sybil攻擊檢測(cè)算法假設(shè)相對(duì)嚴(yán)格并且計(jì)算代價(jià)高，不能有效應(yīng)用在較大規(guī)模的社交網(wǎng)絡(luò)的問題，通過分析Sybil攻擊模型的特點(diǎn)，攻擊節(jié)點(diǎn)需要經(jīng)過攻擊邊對(duì)系統(tǒng)實(shí)施攻擊，使得攻擊邊的邊介數(shù)明顯高于正常邊的邊介數(shù)值，提出一種更加接近于真實(shí)社交網(wǎng)絡(luò)中信息傳播的c-path邊介數(shù)模型，限制隨機(jī)游走路徑的長度，合理選擇路徑出發(fā)點(diǎn)和游走策略，降低計(jì)算復(fù)雜度，提出使得邊介數(shù)性質(zhì)可以應(yīng)用在較大規(guī)模的社交網(wǎng)絡(luò)的邊介數(shù)計(jì)算算法。 其次，針對(duì)現(xiàn)有攻擊檢測(cè)算法沒有有效檢測(cè)惡意用戶團(tuán)體方案的問題，提出一種基于聚類的Sybil團(tuán)體檢測(cè)算法。該算法使用邊介數(shù)結(jié)合邊聚類系數(shù)作為特征，通過k-means算法進(jìn)行聚類，利用種子集中的真實(shí)用戶的數(shù)目確定真實(shí)邊和Sybi攻擊邊的類簇。然后由檢測(cè)得到的Sybil節(jié)點(diǎn)通過標(biāo)簽傳播算法檢測(cè)Sybil節(jié)點(diǎn)所在的惡意團(tuán)體。 最后，在不同的數(shù)據(jù)集上，，將本文提出的Sybil攻擊檢測(cè)方法和現(xiàn)有的檢測(cè)方法進(jìn)行實(shí)驗(yàn)對(duì)比并進(jìn)行分析。
[Abstract]:With the rapid development of social networks, more and more users communicate and share information through social networks. However, because of the openness of social networks, social network users are more vulnerable to security threats, especially Sybil attacks are on the rise. In order to seek benefits, some malicious users create a large number of malicious identities, spread malicious information to real users in social networks or enhance the influence of their own groups, which seriously threaten the security of social networks. Based on the comprehensive analysis of the current research situation at home and abroad, this paper makes a thorough study on how to solve the problem of Sybil attack detection in large-scale social networks. First of all, aiming at the problem that the existing Sybil attack detection algorithms are relatively strict and computationally expensive, and can not be effectively applied to large scale social networks, the characteristics of the Sybil attack model are analyzed. The attack node needs to attack the system through attacking side, which makes the edge medium number of the attack side obviously higher than the normal edge medium value. This paper proposes a c-path edge medium model which is closer to the information propagation in the real social network. This paper limits the length of random walk path, reasonably selects the starting point and walk strategy of the path, reduces the computational complexity, and proposes an edge-medium algorithm that can be applied to large scale social networks. Secondly, aiming at the problem that the existing attack detection algorithms are not effective in detecting malicious user groups, a clustering based Sybil group detection algorithm is proposed. The algorithm uses edge mediums and edge clustering coefficients as the feature, and uses the k-means algorithm to cluster the real users in the seed set to determine the clusters of real edges and Sybi attack edges. Then the detected Sybil node detects the malicious group of Sybil nodes by tag propagation algorithm. Finally, on different data sets, the Sybil attack detection method proposed in this paper and the existing detection methods are compared and analyzed experimentally.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.08

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 李桃迎;陳燕;秦勝君;李楠;;增量聚類算法綜述[J];科學(xué)技術(shù)與工程;2010年35期

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