1,598 research outputs found
Identifying Spam Activity on Public Facebook Pages
Since their emergence, online social networks (OSNs) keep gaining popularity. However, many related problems have also arisen, such as the use of fake accounts for malicious activities. In this paper, we focus on identifying spammers among users that are active on public Facebook pages. We are specifically interested in identifying groups of spammers sharing similar URLs. For this purpose, we built an initial dataset based on all the content that has been posted upon feed posts on a set of public Facebook pages with high numbers of subscribers. We assumed that such public pages, with hundreds of thousands of subscribers and revolving around a common attractive topic, make an ideal ground for spamming activity. Our first contribution in this paper is a reliable methodology that helps in identifying potential spammer and non-spammer accounts that are likely to be tagged as, respectively, spammers/non-spammers upon manual verification. For that aim, we used a set of features characterizing spam activity with a coring method. This methodology, combined with manual human validation, successfully allowed us to build a dataset of spammers and non-spammers. Our second contribution is the analysis of the identified spammer accounts. We found that these accounts do not display any community-like behavior as they rarely interact with each other, and are slightly more active than non-spammers during late-night hours, while slightly less active during daytime hours. Finally, our third contribution is the proposal of a clustering approach that successfully detected 16 groups of spammers in the form of clusters of spam accounts sharing similar URLs
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
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