2,061 research outputs found
Analyzing the Social Structure and Dynamics of E-mail and Spam in Massive Backbone Internet Traffic
E-mail is probably the most popular application on the Internet, with
everyday business and personal communications dependent on it. Spam or
unsolicited e-mail has been estimated to cost businesses significant amounts of
money. However, our understanding of the network-level behavior of legitimate
e-mail traffic and how it differs from spam traffic is limited. In this study,
we have passively captured SMTP packets from a 10 Gbit/s Internet backbone link
to construct a social network of e-mail users based on their exchanged e-mails.
The focus of this paper is on the graph metrics indicating various structural
properties of e-mail networks and how they evolve over time. This study also
looks into the differences in the structural and temporal characteristics of
spam and non-spam networks. Our analysis on the collected data allows us to
show several differences between the behavior of spam and legitimate e-mail
traffic, which can help us to understand the behavior of spammers and give us
the knowledge to statistically model spam traffic on the network-level in order
to complement current spam detection techniques.Comment: 15 pages, 20 figures, technical repor
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Reverse Engineering Socialbot Infiltration Strategies in Twitter
Data extracted from social networks like Twitter are increasingly being used
to build applications and services that mine and summarize public reactions to
events, such as traffic monitoring platforms, identification of epidemic
outbreaks, and public perception about people and brands. However, such
services are vulnerable to attacks from socialbots automated accounts that
mimic real users seeking to tamper statistics by posting messages generated
automatically and interacting with legitimate users. Potentially, if created in
large scale, socialbots could be used to bias or even invalidate many existing
services, by infiltrating the social networks and acquiring trust of other
users with time. This study aims at understanding infiltration strategies of
socialbots in the Twitter microblogging platform. To this end, we create 120
socialbot accounts with different characteristics and strategies (e.g., gender
specified in the profile, how active they are, the method used to generate
their tweets, and the group of users they interact with), and investigate the
extent to which these bots are able to infiltrate the Twitter social network.
Our results show that even socialbots employing simple automated mechanisms are
able to successfully infiltrate the network. Additionally, using a
factorial design, we quantify infiltration effectiveness of different bot
strategies. Our analysis unveils findings that are key for the design of
detection and counter measurements approaches
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