5,605 research outputs found
Detecting Malicious Content on Facebook
Online Social Networks (OSNs) witness a rise in user activity whenever an
event takes place. Malicious entities exploit this spur in user-engagement
levels to spread malicious content that compromises system reputation and
degrades user experience. It also generates revenue from advertisements,
clicks, etc. for the malicious entities. Facebook, the world's biggest social
network, is no exception and has recently been reported to face much abuse
through scams and other type of malicious content, especially during news
making events. Recent studies have reported that spammers earn $200 million
just by posting malicious links on Facebook. In this paper, we characterize
malicious content posted on Facebook during 17 events, and discover that
existing efforts to counter malicious content by Facebook are not able to stop
all malicious content from entering the social graph. Our findings revealed
that malicious entities tend to post content through web and third party
applications while legitimate entities prefer mobile platforms to post content.
In addition, we discovered a substantial amount of malicious content generated
by Facebook pages. Through our observations, we propose an extensive feature
set based on entity profile, textual content, metadata, and URL features to
identify malicious content on Facebook in real time and at zero-hour. This
feature set was used to train multiple machine learning models and achieved an
accuracy of 86.9%. The intent is to catch malicious content that is currently
evading Facebook's detection techniques. Our machine learning model was able to
detect more than double the number of malicious posts as compared to existing
malicious content detection techniques. Finally, we built a real world solution
in the form of a REST based API and a browser plug-in to identify malicious
Facebook posts in real time.Comment: 9 figures, 7 table
The Art of Social Bots: A Review and a Refined Taxonomy
Social bots represent a new generation of bots that make use of online social
networks (OSNs) as a command and control (C\&C) channel. Malicious social bots
were responsible for launching large-scale spam campaigns, promoting low-cap
stocks, manipulating user's digital influence and conducting political
astroturf. This paper presents a detailed review on current social bots and
proper techniques that can be used to fly under the radar of OSNs defences to
be undetectable for long periods of time. We also suggest a refined taxonomy of
detection approaches from social network perspective, as well as commonly used
datasets and their corresponding findings. Our study can help OSN
administrators and researchers understand the destructive potential of
malicious social bots and can improve the current defence strategies against
them
Hiding in Plain Sight: The Anatomy of Malicious Facebook Pages
Facebook is the world's largest Online Social Network, having more than 1
billion users. Like most other social networks, Facebook is home to various
categories of hostile entities who abuse the platform by posting malicious
content. In this paper, we identify and characterize Facebook pages that engage
in spreading URLs pointing to malicious domains. We used the Web of Trust API
to determine domain reputations of URLs published by pages, and identified 627
pages publishing untrustworthy information, misleading content, adult and child
unsafe content, scams, etc. which are deemed as "Page Spam" by Facebook, and do
not comply with Facebook's community standards. Our findings revealed dominant
presence of politically polarized entities engaging in spreading content from
untrustworthy web domains. Anger and religion were the most prominent topics in
the textual content published by these pages. We found that at least 8% of all
malicious pages were dedicated to promote a single malicious domain. Studying
the temporal posting activity of pages revealed that malicious pages were more
active than benign pages. We further identified collusive behavior within a set
of malicious pages spreading adult and pornographic content. We believe our
findings will enable technologists to devise efficient automated solutions to
identify and curb the spread of malicious content through such pages. To the
best of our knowledge, this is the first attempt in literature, focused
exclusively on characterizing malicious Facebook pages.Comment: 11 pages, 9 figures, 6 table
A Survey on Privacy and Security in Online Social Networks
Online Social Networks (OSN) are a permanent presence in today's personal and
professional lives of a huge segment of the population, with direct
consequences to offline activities. Built on a foundation of trust-users
connect to other users with common interests or overlapping personal
trajectories-online social networks and the associated applications extract an
unprecedented volume of personal information. Unsurprisingly, serious privacy
and security risks emerged, positioning themselves along two main types of
attacks: attacks that exploit the implicit trust embedded in declared social
relationships; and attacks that harvest user's personal information for
ill-intended use. This article provides an overview of the privacy and security
issues that emerged so far in OSNs. We introduce a taxonomy of privacy and
security attacks in OSNs, we overview existing solutions to mitigate those
attacks, and outline challenges still to overcome
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
The explosive growth in fake news and its erosion to democracy, justice, and
public trust has increased the demand for fake news detection and intervention.
This survey reviews and evaluates methods that can detect fake news from four
perspectives: (1) the false knowledge it carries, (2) its writing style, (3)
its propagation patterns, and (4) the credibility of its source. The survey
also highlights some potential research tasks based on the review. In
particular, we identify and detail related fundamental theories across various
disciplines to encourage interdisciplinary research on fake news. We hope this
survey can facilitate collaborative efforts among experts in computer and
information sciences, social sciences, political science, and journalism to
research fake news, where such efforts can lead to fake news detection that is
not only efficient but more importantly, explainable.Comment: ACM Computing Surveys (CSUR), 37 page
Web Spam Detection Using Multiple Kernels in Twin Support Vector Machine
Search engines are the most important tools for web data acquisition. Web
pages are crawled and indexed by search Engines. Users typically locate useful
web pages by querying a search engine. One of the challenges in search engines
administration is spam pages which waste search engine resources. These pages
by deception of search engine ranking algorithms try to be showed in the first
page of results. There are many approaches to web spam pages detection such as
measurement of HTML code style similarity, pages linguistic pattern analysis
and machine learning algorithm on page content features. One of the famous
algorithms has been used in machine learning approach is Support Vector Machine
(SVM) classifier. Recently basic structure of SVM has been changed by new
extensions to increase robustness and classification accuracy. In this paper we
improved accuracy of web spam detection by using two nonlinear kernels into
Twin SVM (TSVM) as an improved extension of SVM. The classifier ability to data
separation has been increased by using two separated kernels for each class of
data. Effectiveness of new proposed method has been experimented with two
publicly used spam datasets called UK-2007 and UK-2006. Results show the
effectiveness of proposed kernelized version of TSVM in web spam page
detection
Systems Applications of Social Networks
The aim of this article is to provide an understanding of social networks as
a useful addition to the standard tool-box of techniques used by system
designers. To this end, we give examples of how data about social links have
been collected and used in di erent application contexts. We develop a broad
taxonomy-based overview of common properties of social networks, review how
they might be used in di erent applications, and point out potential pitfalls
where appropriate. We propose a framework, distinguishing between two main
types of social network-based user selection-personalised user selection which
identi es target users who may be relevant for a given source node, using the
social network around the source as a context, and generic user selection or
group delimitation, which lters for a set of users who satisfy a set of
application requirements based on their social properties. Using this
framework, we survey applications of social networks in three typical kinds of
application scenarios: recommender systems, content-sharing systems (e.g., P2P
or video streaming), and systems which defend against users who abuse the
system (e.g., spam or sybil attacks). In each case, we discuss potential
directions for future research that involve using social network properties.Comment: Will appear in ACM computing Survey
Hunting for Spammers: Detecting Evolved Spammers on Twitter
Once an email problem, spam has nowadays branched into new territories with
disruptive effects. In particular, spam has established itself over the recent
years as a ubiquitous, annoying, and sometimes threatening aspect of online
social networks. Due to its prevalent existence, many works have tackled spam
on Twitter from different angles. Spam is, however, a moving target. The new
generation of spammers on Twitter has evolved into online creatures that are
not easily recognizable by old detection systems. With the strong tangled
spamming community, automatic tweeting scripts, and the ability to massively
create Twitter accounts with a negligible cost, spam on Twitter is becoming
smarter, fuzzier and harder to detect. Our own analysis of spam content on
Arabic trending hashtags in Saudi Arabia results in an estimate of about three
quarters of the total generated content. This alarming rate makes the
development of adaptive spam detection techniques a very real and pressing
need. In this paper, we analyze the spam content of trending hashtags on Saudi
Twitter, and assess the performance of previous spam detection systems on our
recently gathered dataset. Due to the escalating manipulation that
characterizes newer spamming accounts, simple manual labeling currently leads
to inaccurate results. In order to get reliable ground-truth data, we propose
an updated manual classification algorithm that avoids the deficiencies of
older manual approaches. We also adapt the previously proposed features to
respond to spammers evading techniques, and use these features to build a new
data-driven detection system
Towards Detecting Compromised Accounts on Social Networks
Compromising social network accounts has become a profitable course of action
for cybercriminals. By hijacking control of a popular media or business
account, attackers can distribute their malicious messages or disseminate fake
information to a large user base. The impacts of these incidents range from a
tarnished reputation to multi-billion dollar monetary losses on financial
markets. In our previous work, we demonstrated how we can detect large-scale
compromises (i.e., so-called campaigns) of regular online social network users.
In this work, we show how we can use similar techniques to identify compromises
of individual high-profile accounts. High-profile accounts frequently have one
characteristic that makes this detection reliable -- they show consistent
behavior over time. We show that our system, were it deployed, would have been
able to detect and prevent three real-world attacks against popular companies
and news agencies. Furthermore, our system, in contrast to popular media, would
not have fallen for a staged compromise instigated by a US restaurant chain for
publicity reasons
We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!
It is completely amazing! Fake news and click-baits have totally invaded the
cyber space. Let us face it: everybody hates them for three simple reasons.
Reason #2 will absolutely amaze you. What these can achieve at the time of
election will completely blow your mind! Now, we all agree, this cannot go on,
you know, somebody has to stop it. So, we did this research on fake
news/click-bait detection and trust us, it is totally great research, it really
is! Make no mistake. This is the best research ever! Seriously, come have a
look, we have it all: neural networks, attention mechanism, sentiment lexicons,
author profiling, you name it. Lexical features, semantic features, we
absolutely have it all. And we have totally tested it, trust us! We have
results, and numbers, really big numbers. The best numbers ever! Oh, and
analysis, absolutely top notch analysis. Interested? Come read the shocking
truth about fake news and click-bait in the Bulgarian cyber space. You won't
believe what we have found!Comment: RANLP'2017, 7 pages, 1 figur
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