本文選題：社交網(wǎng)絡(luò)安全　切入點(diǎn)：Spam帳號(hào)　出處：《西安電子科技大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：社交網(wǎng)絡(luò)的方便快捷共享特性,使其成為人們生活中不可分割的一部分。目前使用社交網(wǎng)絡(luò)展示自己、與好友交流、獲取最新資訊已成為人們的一種習(xí)慣。然而,社交網(wǎng)絡(luò)在帶給人們各種便利的同時(shí)也吸引了攻擊者的目光,成為攻擊者獲取利益的新平臺(tái)。攻擊者通過(guò)在社交網(wǎng)絡(luò)中創(chuàng)建虛假帳號(hào)以及劫持正常帳號(hào)(我們統(tǒng)稱為異常帳號(hào))來(lái)發(fā)布廣告、色情、釣魚等惡意消息以及執(zhí)行惡意點(diǎn)贊、批量關(guān)注等行為來(lái)獲取利益,這些惡意行為嚴(yán)重影響威脅到正常用戶的隱私信息安全、使用體驗(yàn)以及社交網(wǎng)絡(luò)平臺(tái)自身的信譽(yù)體系。針對(duì)這些問(wèn)題,我們展開(kāi)了在線社交網(wǎng)絡(luò)中異常帳號(hào)檢測(cè)的工作,重點(diǎn)研究在線社交網(wǎng)絡(luò)中新出現(xiàn)的Photo Spam攻擊方式的檢測(cè),并取得了如下一些主要成果：(1)分析總結(jié)了目前在線社交網(wǎng)絡(luò)中異常帳號(hào)檢測(cè)的研究工作。將異常帳號(hào)的生命周期分為創(chuàng)建、發(fā)展、應(yīng)用三個(gè)階段,然后根據(jù)異常帳號(hào)的表現(xiàn)形式將不同稱謂的異常帳號(hào)統(tǒng)一在同一個(gè)框架中；總結(jié)了目前異常帳號(hào)檢測(cè)研究的實(shí)驗(yàn)方法,包括數(shù)據(jù)獲取方式、數(shù)據(jù)標(biāo)識(shí)方式和結(jié)果驗(yàn)證方式；在此基礎(chǔ)上深入分析了社交網(wǎng)絡(luò)中新的攻擊方式Photo Spam,分析了Photo Spam的攻擊過(guò)程和攻擊策略,并對(duì)比了Photo Spam與傳統(tǒng)Spam,發(fā)現(xiàn)與傳統(tǒng)Spam攻擊相比,Photo Spam更難被檢測(cè)到而且對(duì)正常用戶的危害更大。(2)提出一種專門針對(duì)Photo Spam帳號(hào)的檢測(cè)方案。Photo Spam是攻擊者為了繞過(guò)社交網(wǎng)絡(luò)現(xiàn)有檢測(cè)系統(tǒng)的新式Spam攻擊,具有Spam信息的存儲(chǔ)與傳播分離的特性,在攻擊過(guò)程中有兩類行為方式不同的Spam帳號(hào)參與。目前對(duì)Photo Spam的檢測(cè)方案都是根據(jù)帳號(hào)行為方式進(jìn)行檢測(cè),無(wú)法將兩類Spam帳號(hào)都檢測(cè)到。針對(duì)這一問(wèn)題,我們首次提出了一種專門針對(duì)Photo Spam帳號(hào)的檢測(cè)方案。首先通過(guò)對(duì)Photo Spam攻擊的分析構(gòu)造了基于用戶信息和基于內(nèi)容兩方面的特征；然后利用這些特征設(shè)計(jì)了有監(jiān)督學(xué)習(xí)的檢測(cè)方案,通過(guò)包含2,046個(gè)帳號(hào)的數(shù)據(jù)集訓(xùn)練成為專門針對(duì)Photo Spam帳號(hào)的分類器,我們的分類器能夠檢測(cè)全部類型的Photo Spam帳號(hào)；最后將訓(xùn)練后的分類器應(yīng)用到包含有85,148個(gè)帳號(hào)的真實(shí)數(shù)據(jù)集中,共檢測(cè)到5,756個(gè)Photo Spam帳號(hào),檢測(cè)正確率為97.05%。(3)提出一種針對(duì)Photo Spam帳號(hào)的輕量級(jí)迭代檢測(cè)算法。社交網(wǎng)絡(luò)為了保護(hù)正常用戶的個(gè)人信息安全和使用體驗(yàn),需要在有限的時(shí)間內(nèi)降低Spam帳號(hào)的比例,而目前采用數(shù)據(jù)挖掘的檢測(cè)方案要對(duì)所有用戶都進(jìn)行深入檢測(cè),將耗費(fèi)大量的時(shí)間和機(jī)器成本,無(wú)法滿足這一現(xiàn)實(shí)需求。針對(duì)這一問(wèn)題,我們首次提出一種針對(duì)Photo Spam帳號(hào)的輕量級(jí)迭代檢測(cè)算法LIDA。LIDA包括目標(biāo)篩選和內(nèi)容檢測(cè)2個(gè)步驟,通過(guò)目標(biāo)篩選根據(jù)已知Spam帳號(hào)獲取更多可疑帳號(hào),通過(guò)內(nèi)容檢測(cè)對(duì)可疑帳號(hào)進(jìn)行深入檢測(cè)判斷是否的確為Spam帳號(hào)。LIDA只對(duì)可疑帳號(hào)進(jìn)行深入檢測(cè),避免了對(duì)社交網(wǎng)絡(luò)中所有用戶都進(jìn)行檢測(cè)的問(wèn)題,實(shí)現(xiàn)了對(duì)Photo Spam帳號(hào)的輕量級(jí)檢測(cè)。通過(guò)人人網(wǎng)的4次迭代實(shí)驗(yàn),共檢測(cè)到9,568個(gè)Spam帳號(hào),檢出率為18.84%,比基于數(shù)據(jù)挖掘的檢測(cè)算法更加高效。(4)提出一種針對(duì)社交網(wǎng)絡(luò)中Spam相冊(cè)的檢測(cè)方案。目前檢測(cè)Photo Spam的方案都是針對(duì)Spam帳號(hào)進(jìn)行檢測(cè),檢測(cè)依據(jù)主要是帳號(hào)的惡意行為,因此需要Spam帳號(hào)存在一定時(shí)間之后才能夠檢測(cè)到,而在此期間Spam帳號(hào)的惡意行為已經(jīng)對(duì)正常用戶造成了危害,所以針對(duì)Spam帳號(hào)的檢測(cè)方案滯后于Spam攻擊,無(wú)法有效保護(hù)正常用戶。針對(duì)這一問(wèn)題,我們首次提出一種針對(duì)Spam相冊(cè)的檢測(cè)方案。首先基于Spam相冊(cè)和正常相冊(cè)的差異構(gòu)造了12個(gè)提取及時(shí)且計(jì)算高效的特征；然后通過(guò)這些特征設(shè)計(jì)了針對(duì)Spam相冊(cè)的檢測(cè)模型；利用包含2,356個(gè)相冊(cè)的數(shù)據(jù)集訓(xùn)練形成Spam相冊(cè)分類器,實(shí)驗(yàn)表明能夠正確區(qū)分測(cè)試集中100%的Spam相冊(cè)和98.2%的正常相冊(cè)；最后將檢測(cè)模型應(yīng)用到包含315,115個(gè)相冊(cè)的真實(shí)數(shù)據(jù)集中,共檢測(cè)到89,163個(gè)Spam相冊(cè),正確率達(dá)到94.2%。
[Abstract]:The social network convenient sharing characteristics, make it become an integral part of people's life. At present, the use of social networks to show their communication with friends, get the latest information has become a habit of people. However, in the social network to bring people convenience at the same time also attracted the attacker's eyes become a new platform for the attacker getting benefits. Attackers use in social networks to create a false account and account hijacking normal (we referred to as abnormal account) to publish advertisements, pornography, phishing and other malicious messages and execute malicious praise, batch attention acts to get benefits, these malicious behavior seriously affect the privacy of information security threats to the normal user the use of experience and social networking platform, its own credit system. To solve these problems, we launched the online social network account abnormal detection work, heavy Detection of Photo Spam attack to new research in online social networks, and the following conclusions: (1) analyzed and summarized the current account in the online social network abnormal detection research. The abnormal account life cycle is divided into creation, development, application of three stages, unified account and abnormal according to the form of abnormal account will different titles in the same framework; summarizes the current research of detecting abnormal account methods, including data acquisition, data identification and verification results; on the basis of in-depth analysis of the new attack methods in social network Photo Spam, analyzes the attack process and attack strategy Photo Spam and Photo Spam, compared with the traditional Spam, and found that the traditional Spam attack, Photo Spam is more difficult to be detected and the harm to the normal user more. (2) proposed A specific Photo Spam account.Photo Spam detection scheme is the attacker to bypass the existing social network detection system of the new Spam attack, characteristics of storage and transmission with Spam information, there are two types of behavior of different Spam account participation in the process of attack. The current detection scheme of Photo Spam are tested according to the account behavior cannot be two Spam accounts are detected. To solve this problem, we propose a Spam account specifically for Photo detection scheme. By analyzing the Photo Spam attack is constructed based on user information and based on the characteristics of the two aspects of the content; and then use these features to design a detection scheme supervised learning, by including the 2046 account data set training is specifically for the Photo Spam account classifier, our classifier can detect all Type Photo Spam account; finally by the trained classifier to contain real data of 85148 accounts, 5756 Photo Spam accounts were detected. The detection accuracy is 97.05%. (3) proposed a lightweight Spam account for Photo iterative detection algorithm. The social network in order to the security of personal information and experience to protect the normal users, the need to reduce the proportion of Spam account for a limited time, and the detection scheme of data mining should be carried out in-depth inspection of all users will spend a lot of time and cost of the machine, can not meet the realistic demand. To solve this problem, we propose a lightweight iteration for LIDA.LIDA Photo Spam account detection algorithm including the target selection and content detection of 2 steps, through the target screening according to the known Spam account for the more suspicious account through content Detection of suspicious account further detecting and judging whether does Spam.LIDA account only for further detection of suspicious account, to avoid all the users in the social network testing problem, realize the lightweight detection of the Photo Spam account. By 4 iterations the renren.com, 9568 Spam accounts were detected, the detection the rate is 18.84%, the detection algorithm based on data mining more efficient. (4) proposed a scheme for detection of social network Spam album. At present the detection of Photo Spam scheme is to detect the Spam account, according to the detection of malicious behavior is the main account, so we need to Spam account for a certain period of time to be able to detected, while the malicious behavior of normal user Spam account has been damaged, so the detection scheme for the Spam account is behind the Spam attack, can not effectively protect it Ordinary users. To solve this problem, we first proposed a detection scheme for Spam photo album. The first difference between Spam and normal album album based on the structure characteristics of the 12 extracted timely and efficient calculation; then based on these features, design a detection model for Spam photo album; training set form contains 2356 Spam photo album using classifier the album data, experiments show that the normal album can correctly differentiate the test set 100% Spam album and 98.2%; the detection model is applied to the real data contains 315115 albums, 89163 Spam albums were detected, the correct rate of 94.2%.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP393.09

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