本文選題：社交網(wǎng)絡(luò)安全　切入點：Spam帳號　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也吸引了攻擊者的目光,成為攻擊者獲取利益的新平臺。攻擊者通過在社交網(wǎng)絡(luò)中創(chuàng)建虛假帳號以及劫持正常帳號(我們統(tǒng)稱為異常帳號)來發(fā)布廣告、色情、釣魚等惡意消息以及執(zhí)行惡意點贊、批量關(guān)注等行為來獲取利益,這些惡意行為嚴重影響威脅到正常用戶的隱私信息安全、使用體驗以及社交網(wǎng)絡(luò)平臺自身的信譽體系。針對這些問題,我們展開了在線社交網(wǎng)絡(luò)中異常帳號檢測的工作,重點研究在線社交網(wǎng)絡(luò)中新出現(xiàn)的Photo Spam攻擊方式的檢測,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)中異常帳號檢測的研究工作。將異常帳號的生命周期分為創(chuàng)建、發(fā)展、應(yīng)用三個階段,然后根據(jù)異常帳號的表現(xiàn)形式將不同稱謂的異常帳號統(tǒng)一在同一個框架中；總結(jié)了目前異常帳號檢測研究的實驗方法,包括數(shù)據(jù)獲取方式、數(shù)據(jù)標(biāo)識方式和結(jié)果驗證方式；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的攻擊過程和攻擊策略,并對比了Photo Spam與傳統(tǒng)Spam,發(fā)現(xiàn)與傳統(tǒng)Spam攻擊相比,Photo Spam更難被檢測到而且對正常用戶的危害更大。(2)提出一種專門針對Photo Spam帳號的檢測方案。Photo Spam是攻擊者為了繞過社交網(wǎng)絡(luò)現(xiàn)有檢測系統(tǒng)的新式Spam攻擊,具有Spam信息的存儲與傳播分離的特性,在攻擊過程中有兩類行為方式不同的Spam帳號參與。目前對Photo Spam的檢測方案都是根據(jù)帳號行為方式進行檢測,無法將兩類Spam帳號都檢測到。針對這一問題,我們首次提出了一種專門針對Photo Spam帳號的檢測方案。首先通過對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)計了有監(jiān)督學(xué)習(xí)的檢測方案,通過包含2,046個帳號的數(shù)據(jù)集訓(xùn)練成為專門針對Photo Spam帳號的分類器,我們的分類器能夠檢測全部類型的Photo Spam帳號；最后將訓(xùn)練后的分類器應(yīng)用到包含有85,148個帳號的真實數(shù)據(jù)集中,共檢測到5,756個Photo Spam帳號,檢測正確率為97.05%。(3)提出一種針對Photo Spam帳號的輕量級迭代檢測算法。社交網(wǎng)絡(luò)為了保護正常用戶的個人信息安全和使用體驗,需要在有限的時間內(nèi)降低Spam帳號的比例,而目前采用數(shù)據(jù)挖掘的檢測方案要對所有用戶都進行深入檢測,將耗費大量的時間和機器成本,無法滿足這一現(xiàn)實需求。針對這一問題,我們首次提出一種針對Photo Spam帳號的輕量級迭代檢測算法LIDA。LIDA包括目標(biāo)篩選和內(nèi)容檢測2個步驟,通過目標(biāo)篩選根據(jù)已知Spam帳號獲取更多可疑帳號,通過內(nèi)容檢測對可疑帳號進行深入檢測判斷是否的確為Spam帳號。LIDA只對可疑帳號進行深入檢測,避免了對社交網(wǎng)絡(luò)中所有用戶都進行檢測的問題,實現(xiàn)了對Photo Spam帳號的輕量級檢測。通過人人網(wǎng)的4次迭代實驗,共檢測到9,568個Spam帳號,檢出率為18.84%,比基于數(shù)據(jù)挖掘的檢測算法更加高效。(4)提出一種針對社交網(wǎng)絡(luò)中Spam相冊的檢測方案。目前檢測Photo Spam的方案都是針對Spam帳號進行檢測,檢測依據(jù)主要是帳號的惡意行為,因此需要Spam帳號存在一定時間之后才能夠檢測到,而在此期間Spam帳號的惡意行為已經(jīng)對正常用戶造成了危害,所以針對Spam帳號的檢測方案滯后于Spam攻擊,無法有效保護正常用戶。針對這一問題,我們首次提出一種針對Spam相冊的檢測方案。首先基于Spam相冊和正常相冊的差異構(gòu)造了12個提取及時且計算高效的特征；然后通過這些特征設(shè)計了針對Spam相冊的檢測模型；利用包含2,356個相冊的數(shù)據(jù)集訓(xùn)練形成Spam相冊分類器,實驗表明能夠正確區(qū)分測試集中100%的Spam相冊和98.2%的正常相冊；最后將檢測模型應(yīng)用到包含315,115個相冊的真實數(shù)據(jù)集中,共檢測到89,163個Spam相冊,正確率達到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)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP393.09

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