博碩士論文 101522068 詳細資訊




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姓名 顏孟加(Meng-Jia Yan)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱
(Itus: Behavior-based Spamming Groups Detection on Facebook)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2019-6-30以後開放)
摘要(中) Facebook為目前全球最大的社交網路,其每日活躍用戶總數超過8.02億人。不幸的是,Facebook也成為攻擊者們的目標,攻擊者利用Facebook傳播大量的釣魚訊息,並從中獲利。其中一個被濫用來散播訊息的管道為社團(Group)服務,由於社團邀請機制能夠不經朋友的允許將朋友加入社團中,一旦攻擊者盜用了正常使用者的帳號,便能迅速地將這些使用者的朋友們通通加入這些惡意社團,導致這些朋友也成為了受害者之一。

本篇論文著重在偵測Facebook中惡意散播垃圾訊息的社團,這些社團大多利用販賣便宜的衣服、電子產品、藥物等資訊,試圖吸引受害者提供個人資訊,甚至不透過大型線上拍賣網站的認證步驟,直接要求受害者轉帳。使用者可以透過Facebook官方回報機制檢舉這些擾人的社團,但是其效率並不高,在我們的實驗過程中發現,大部分的惡意社團至少存活了5個月以上。

我們開發了一個應用程式-Itus,目的是自動、即時地為使用者找出已經加入的社團中是否存在著惡意社團。除了使用Facebook API取得使用者的社團資訊、成員互動程度之外,更進一步地分析成員間的邀請紀錄,將被攻擊者濫用的邀請機制轉化為偵測方式。我們使用support vector machine進行資料訓練及預測,實驗結果顯示邀請紀錄能夠有效地改善Itus的準確率,且誤判率在目前存在的自動偵測惡意社團機制中是最低的。
摘要(英) Facebook is the largest online social network, and total number of daily active users on Facebook is more than 802 million in March 2014. Unfortunately, attackers are also expanding their territory to Facebook to propagate spam. One of the ways to propagate spam on Facebook is using Facebook Groups.

Group’s members can invite their friends to join the Group without invitees’ permission. However, questions then arise about the friendly invitation mechanism. Using fake or compromised accounts, attackers can spread invitation to all friends, that is, not only the compromised account, but all his friends become the victims. Then the victims start to receive notifications by default when any member posts in the Group’s Wall, even though they have not visited these Groups.

The Facebook report mechanism cannot effectively detect spamming Groups. Many active spamming Groups have survived for five months at least. In this paper, we develop Itus to identify spamming Groups and protect Facebook users from them. In addition to extracting the static features from Facebook Groups, we are concerned with relationship between members and social activities in a Group. This work is hard to implement because we have to crawl the Group’s invitation records manually to find out the relations of members which Facebook does not provide due to the privacy concern.

The invitation records are major contributors to improve accuracy of our mechanism. Experimental results employed a support vector machine (SVM) on identifying spamming Groups, showing that the best total error rate of Itus is 3.27%. In the future, we will try to cooperate with Facebook, accessing these sensitive data which have become anonymous to prevent users’ personal information from being breached and illegally used.
關鍵字(中) ★ 社交網路
★ 臉書
★ 購物社團
關鍵字(英) ★ online social network
★ Facebook
★ Spamming Group
論文目次 摘要 i
Abstract ii
Acknowledgement iii
Table of Contents iv
List of Figures vi
List of Tables vii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Goal 3
1.3 Thesis organization 4
Chapter 2 Background 5
2.1 The Glossary of Facebook Terminology 5
2.2 The Difference between Facebook Groups and Pages 6
2.3 The Observed Characteristics of Spamming Groups 8
Chapter 3 Design of Itus 10
3.1 Itus Components 10
3.2 Feature description 12
Chapter 4 Implementation 16
4.1 Environment Setup 16
4.2 Permission 18
4.3 Auxiliary Crawling Program 20
4.4 Classifier 22
Chapter 5 Experimental Results 24
5.1 Implementation 25
5.2 Performance 26
5.3 Accuracy 26
Chapter 6 Discussion 29
6.1 Limitations and Future work 29
6.2 Comparison 29
Chapter 7 Conclusion 32
Reference 33
參考文獻 [1] Facebook Newsroom. Company Info. http://newsroom.fb.com/company-info/
[2] Ya-Shan You. A Study on Facebook for Spamming Group Detection. National Tsing Hua University, August, 2013.
[3] Criminal Investigation Bureau. Fraud uncovered on Facebook Group. http://www.cib.gov.tw/english/News/Detail/29461
[4] Criminal Investigation Bureau. Fraud on Facebook Group increased. http://www.cib.gov.tw/News/BulletinDetail/2642
[5] Facebook. What is Facebook doing to protect me from spam? https://www.facebook.com/help/637109102992723/
[6] Christian Kreibich, Chris Kanich, Kirill Levchenko, Brandon Enright, Geoffrey M. Voelker, Vern Paxson, and Stefan Savage. Spamcraft: an inside look at spam campaign orchestration. In Proceedings of the 2nd USENIX conference on Large-scale exploits and emergent threats: botnets, spyware, worms, and more, 2009
[7] Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten, and Ivan Osipkov. Spamming botnets: signatures and characteristics. In Proceedings of the ACM SIGCOMM conference on Data communication, 2008.
[8] Md Sazzadur Rahman, Ting-Kai Huang, Harsha V. Madhyastha, Michalis Faloutsos. Efficient and Scalable Socware Detection in Online Social Networks. USENIX Security Symposium, 2012.
[9] TonyQ. Facebook Advertisement Checker. http://spamGroup.tonyq.org/
[10] Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen, Ben Y. Zhao. Detecting and characterizing social spam campaigns. ACM Conference on Computer and Communications Security, 2010.
[11] Facebook. Facebook Developers. https://developers.facebook.com/
[12] Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng and Ben Y. Zhao. You are How You Click: Clickstream Analysis for Sybil Detection. USENIX Security Symposium, 2013.
[13] Facebook. Facebook SDK for JavaScript. https://developers.facebook.com/docs/javascript/quickstart/v2.0
[14] Chrome. What are extensions?
https://developer.chrome.com/extensions
[15] Chih-Chung Chang and Chih-Jen Lin. LIBSVM -- A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/
[16] Facebook. Platform Status. https://developers.facebook.com/status/
[17] TonyQ. Open-source Code of Facebook Advertisement Checker. https://github.com/tony1223/spam-group
指導教授 許富皓(Fu-Hau Hsu) 審核日期 2014-7-25
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