Online Social Networks (OSNs) have become an integral
part of today's Web. Politicians, celebrities, revolutionists,
and others use OSNs as a podium to deliver their message
to millions of active web users. Unfortunately, in the wrong
hands, OSNs can be used to run astroturf campaigns to
spread misinformation and propaganda. Such campaigns
usually start o by inltrating a targeted OSN on a large
scale. In this paper, we evaluate how vulnerable OSNs are
to a large-scale inltration by socialbots: computer programs
that control OSN accounts and mimic real users. We adopt
a traditional web-based botnet design and built a Socialbot
Network (SbN): a group of adaptive socialbots that are or-
chestrated in a command-and-control fashion. We operated
such an SbN on Facebook|a 750 million user OSN|for
about 8 weeks. We collected data related to users' behav-
ior in response to a large-scale inltration where socialbots
were used to connect to a large number of Facebook users.
Our results show that (1) OSNs, such as Facebook, can be
inltrated with a success rate of up to 80%, (2) depending
on users' privacy settings, a successful inltration can result
in privacy breaches where even more users' data are exposed
when compared to a purely public access, and (3) in prac-
tice, OSN security defenses, such as the Facebook Immune
System, are not eective enough in detecting or stopping a
large-scale inltration as it occurs.
%0 Conference Paper
%1 boshmaf2011socialbot
%A Boshmaf, Yazan
%A Muslukhov, Ildar
%A Beznosov, Konstantin
%A Ripeanu, Matei
%B Proc. of the Annual Computer Security Applications Conference 2011
%D 2011
%I ACM
%K anaylsis bots facebook network socialbot socialize toread
%T The Socialbot Network: When Bots Socialize for Fame and Money
%U http://lersse-dl.ece.ubc.ca/record/264/files/ACSAC_2011.pdf
%X Online Social Networks (OSNs) have become an integral
part of today's Web. Politicians, celebrities, revolutionists,
and others use OSNs as a podium to deliver their message
to millions of active web users. Unfortunately, in the wrong
hands, OSNs can be used to run astroturf campaigns to
spread misinformation and propaganda. Such campaigns
usually start o by inltrating a targeted OSN on a large
scale. In this paper, we evaluate how vulnerable OSNs are
to a large-scale inltration by socialbots: computer programs
that control OSN accounts and mimic real users. We adopt
a traditional web-based botnet design and built a Socialbot
Network (SbN): a group of adaptive socialbots that are or-
chestrated in a command-and-control fashion. We operated
such an SbN on Facebook|a 750 million user OSN|for
about 8 weeks. We collected data related to users' behav-
ior in response to a large-scale inltration where socialbots
were used to connect to a large number of Facebook users.
Our results show that (1) OSNs, such as Facebook, can be
inltrated with a success rate of up to 80%, (2) depending
on users' privacy settings, a successful inltration can result
in privacy breaches where even more users' data are exposed
when compared to a purely public access, and (3) in prac-
tice, OSN security defenses, such as the Facebook Immune
System, are not eective enough in detecting or stopping a
large-scale inltration as it occurs.
@inproceedings{boshmaf2011socialbot,
abstract = {Online Social Networks (OSNs) have become an integral
part of today's Web. Politicians, celebrities, revolutionists,
and others use OSNs as a podium to deliver their message
to millions of active web users. Unfortunately, in the wrong
hands, OSNs can be used to run astroturf campaigns to
spread misinformation and propaganda. Such campaigns
usually start o by inltrating a targeted OSN on a large
scale. In this paper, we evaluate how vulnerable OSNs are
to a large-scale inltration by socialbots: computer programs
that control OSN accounts and mimic real users. We adopt
a traditional web-based botnet design and built a Socialbot
Network (SbN): a group of adaptive socialbots that are or-
chestrated in a command-and-control fashion. We operated
such an SbN on Facebook|a 750 million user OSN|for
about 8 weeks. We collected data related to users' behav-
ior in response to a large-scale inltration where socialbots
were used to connect to a large number of Facebook users.
Our results show that (1) OSNs, such as Facebook, can be
inltrated with a success rate of up to 80%, (2) depending
on users' privacy settings, a successful inltration can result
in privacy breaches where even more users' data are exposed
when compared to a purely public access, and (3) in prac-
tice, OSN security defenses, such as the Facebook Immune
System, are not eective enough in detecting or stopping a
large-scale inltration as it occurs.},
added-at = {2011-11-02T15:16:51.000+0100},
author = {Boshmaf, Yazan and Muslukhov, Ildar and Beznosov, Konstantin and Ripeanu, Matei},
biburl = {https://www.bibsonomy.org/bibtex/2a6ef16ba759ee4c56ccd4d017560344e/hotho},
booktitle = {Proc. of the Annual Computer Security Applications Conference 2011},
interhash = {d384da66292051fab7eca0372805c9af},
intrahash = {a6ef16ba759ee4c56ccd4d017560344e},
keywords = {anaylsis bots facebook network socialbot socialize toread},
publisher = {ACM},
timestamp = {2011-11-02T15:16:52.000+0100},
title = {The Socialbot Network: When Bots Socialize for Fame and Money},
url = {http://lersse-dl.ece.ubc.ca/record/264/files/ACSAC_2011.pdf},
year = 2011
}