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A Survey of Social Network Security Issues

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Hongyu Gao, Tuo Huang, Jun Hu, Jingnan Wang Active detection ---Social Honeypots Steve et al Message spam and comment spam are similar with traditional spam.In my ... – PowerPoint PPT presentation

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Title: A Survey of Social Network Security Issues


1
A Survey of Social Network Security Issues
  • Hongyu Gao, Tuo Huang, Jun Hu, Jingnan Wang

2
History of Social Network Sites
  • Boyd et al. Social Network Sites Definition,
    History, and Scholarship. Journal of
    Computer-Mediated Communication, 13(1), article
    11. 2007

3
Explosion of Social Network
  • Rapid growth of social network sites spawns a new
    area of network security and privacy issues

4
Purpose of the Study
  • To conduct a comprehensive survey of existing and
    potential attack behaviors in social network
    sites
  • Identify patterns in such attack behaviors
  • Review existing solutions, measurement as well as
    defense mechanisms

5
Security Issues Identified
  • Social Engineering attacks
  • Spamming
  • Phishing
  • Social Network vs. Social Network Sites (SNS)
  • Sybil attack
  • Social network Account Attack
  • Hack the social network account using password
    cracking.
  • Malware attack
  • Social Network sites as vectors of malware
    propagation

6
Spamming
  • SNS as vectors for conventional spamming
  • Messages, Wallposts, Comments,
  • Detection and measurements

7
Active detection ---Social Honeypots Steve et al
  • Message spam and comment spam are similar with
    traditional spam.In my space there is new form of
    spam deceptive profile spam.
  • This kind of spammer uses sexy photo and
    seductive story in about me section to attract
    visitors.

8
Social honeypots
Figure 1 An example of a deceptive spam profile
9
Social honeypots
  • Social honeypots can be seen as a kind of active
    detection of social network spam.
  • The author constructed 51 honeypot profiles and
    associated them with distinct geographic location
    in Myspace to collect the deceptive spam
    profiles.
  • For the num of their honeypots is small,so the
    dataset they collected is very limited.

10
Passive detection-----Detecting spammers and
content promoters in online video social
networks, by F. Benevenuto, et. al.
  • This paper is a comprehensive behavior-based
    detection and it can be cataloged into passive
    dectection compared with Social Honeypots.

11
Passive detection
  • The author manually select a test collection of
    real YouTube users, classifying them  as
    spammers, promoters, and legitimates. Using this
    collection,they provided a characterization of
    social and content attributes that help
    distinguish each user class.They used a
    state-of-the-art supervised classification
    algorithm to detect spammers and promoters, and
    assess its effectiveness in their test
    collection.

12
Passive detection
  • They considered three attribute sets, namely,
    video attributes, user attributes, and social
    network (SN) attributes.

13
Passive detection
  • They characterize each video by its duration,
    numbers of views and of commentaries received,
    ratings, number of times the video was selected
    as favorite, as well as numbers of honors and of
    external links

14
Passive detection
  • They select the following 10 user attributes
    number of friends, number of videos Uploaded,
    number of videos watched, number of videos added
    as favorite, numbers of video responses posted
    and received, numbers of subscriptions and
    subscribers, average time between video uploads,
    and maximum number of videos uploaded in 24 hours.

15
Passive detection
  • Social network (SN) attributes clustering
    coefficient, betweenness,reciprocity,
    assortativity, and UserRank.

16
Passive detection
  • For it is passive detection,it need pre-knowledge
    and another drawback is that using supervised
    learning algorithm may require large dataset for
    learning, otherwise the result will not be
    accurate.

17
Social Spamming
  • Characteristics
  • No specific recipient
  • Using SNS as free advertisement site
  • Can completely undermine the service of the
    website especially if launched as Sybil attacks
  • Detection Metrics
  • TagSpam
  • TagBlur
  • DomFp
  • NumAds
  • ValidLink

18
Sybil Attack
  • A general form of attack to reputation systems
  • Large amount of fake identities outvote honest
    identities
  • Can be used to thwart the intended purpose of
    certain SNSes

19
Defense to Sybil Attacks SybilGuard Yu et al.
  • Sybil Nodes have small Quotient Cuts
  • Inherent social networks do not
  • Possible to encircle the Sybil nodes

20
Malware attack
  • The most notorious worm in social network is the
    koobface. According to Trend Micro, the attack
    from koobface as follows

Step 1 Registering a Facebook account. Step
2Confirming an e-mail address in Gmail to
activate the registered account. Step 3 Joining
random Facebook groups. Step 4 Adding friends
and posting messages on their walls.
21
Malware attack
  • There are worms and other threats that have
    plagued social networking sites. E.g. Grey Goo
    targeting at Second Life, JS/SpaceFlash targeting
    at MySpace,Kut Wormer targeting at Orkut, Secret
    Crush targeting at Facebook, etc.

22
Malware attack
  • Until now there are few papers on detecting these
    attacks.

23
Social network Account Attack
  • Hack the social network account using password
    cracking.

-----In February,2009, the Twitter account of
Miley Cyrus was hijacked too and someone posted
some offensive messages
24
And more to be discovered
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