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Gianluca Stringhini, Christopher Kruegel, Giovanni Vigna
University of California, Santa Barbara
26th ACSAC(December, 2010)
Outline
Introduction
 The Popular Social Networks
 Data Collection
 Analysis of Collected Data
 Spam Profile Detection

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Introduction

Sites such as Facebook, MySpace, and
Twitter are consistently among the top
20 most-viewed web sites of the Internet.

In 2008, 83% of the users of social
networks have received at least one
unwanted friend request or message.
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Introduction

A previous study showed that 45% of
users on a social networking site readily
click on links posted by their “friend”
accounts, even if they do not know that
person in real life.
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Security Threat[link]
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The Popular Social Networks

Facebook
 the Facebook administrators claim to have
more than 400 million active users all over
the world.
 The default privacy setting was to allow all
people in the same network to view each
other’s profiles.
○ Facebook deprecated geographic networks in
October 2009.
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The Popular Social Networks

MySpace
 MySpace pages are public by default.

Twitter
 It is designed as a microblogging platform,
where users send short text messages (i.e.,
tweets) that appear on their friends’ pages.
 Users are identified only by a username and,
optionally, by a real name.
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Data Collection

We created 900 profiles on Facebook,
MySpace, and Twitter, 300 on each
platform.

Due to the similarity of these profiles to
honeypots, we call these accounts
honey-profiles.
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Honey-Profiles

Facebook
 we joined 16 geographic networks, using a
small number of manually-created accounts.
 For each network, we crawled 2,000
accounts at random, logging names, ages,
and gender.
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Honey-Profiles

MySpace
 we crawled 4,000 accounts in total.

Twitter
 the only information required for signing up
is a full name and a profile name.
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Collection of Data

We did not send any friend requests, but
accepted all those that were received.

Our scripts ran continuously for 12
months for Facebook (from June 6,
2009 to June 6, 2010), and for 11
months for MySpace and Twitter (from
June 24, 2009 to June 6, 2010).
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Analysis of Collected Data
Looking for
“local” friends
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Analysis of Collected Data: Facebook
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Analysis of Collected Data: Twitter
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Spam Bot Analysis

Displayer
 Bots that only display some spam content
on their own profile pages.

Bragger
 Bots that post messages to their own feed.
Poster
 Whisperer

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Spam Bot Analysis
Facebook
MySpace
Twitter
Displayer
2
8
0
Bragger
163
0
341
Poster
8
0
0
Whisperer
0
0
20
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Spam Bot Analysis

On Facebook, the average lifetime of a
spam account was four days, while on
Twitter, it was 31 days.

During our observation, we noticed that
some bots showed a higher activity
around midnight.
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Spam Bot Analysis

We identified two kinds of bot behavior
 Greedy :416
 Stealthy: 98

Most spam profiles we observed, both
on Facebook and Twitter, sent less than
20 messages during their life span.
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Spam Bot Analysis

While observing Facebook spammers,
we also noticed that many of them did
not seem to pick victims randomly.

80% of bots we detected on Facebook
used the mobile interface of the site to
send their spam messages.
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Spam Profile Detection

FF ratio (R)
 The feature compares the number of friend
requests that a user sent to the number of
friends she has.
○ Unfortunately, the number of friend requests
sent is not public on Facebook and on
MySpace.
○ R = following / followers
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Spam Profile Detection

URL ratio (U)
 The feature to detect a bot is the presence
of URLs in the logged messages.
 U = messages containing urls / total
messages.
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Spam Profile Detection

Message Similarity (S)


Friend Choice (F)

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Spam Profile Detection

Messages Sent (M)

Friend Number (FN)

We used the Weka framework with a
Random Forest algorithm[link] for our
classifier.
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Detection on Facebook

Using 1,000 profiles
 173 spam bots that contacted our honey-
profiles
 827 manually checked profiles

From 790,951 profiles
 Detected: 130
 False positive: 7

From 100 profiles
 False negative: 0
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Detection on Twitter
We picked 500 spam profiles.
 We also picked 500 legitimate profiles.


Twitter limited our machine to execute
only 20,000 API calls per hour.
 we executed Google searches for the most
common words in tweets sent by the already
detected spammers
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Detection on Twitter

From March 06, 2010 to June 06, 2010,
we crawled 135,834 profiles, detecting
15,932 of those as spammers.
 False positive: 75
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Identification of Spam
Campaigns

We consider two bots posting messages
with URLs pointing to the same site as
being part of the same campaign.
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Spam Campaigns Observed
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