Adapting Social Spam Infrastructure for Political Censorship Report

This research paper investigates the adaptation of social spam infrastructure for political censorship, focusing on a cyber-attack on Twitter involving 25,860 fraudulent accounts. The study analyzes how malicious actors leverage spam-as-a-service to manipulate political discourse and disrupt online conversations. The authors deconstruct the attack, examining the tweets, registration data, and IP addresses used to spread misinformation and control political expression. The paper highlights the techniques employed by hackers, including the use of automated accounts and spam campaigns, and provides insights into the infrastructure and accounts involved. The analysis includes an examination of the strategies used to detect and manage such attacks, emphasizing the importance of understanding the evolving tactics of online manipulation. The research underscores the vulnerabilities of social networks to censorship and the need for robust security measures to protect against cyber-attacks and data breaches.

RESEARCH PAPER 1
Kurt Thomas, Chris Grier, and Vern Paxson
Abstract-A social network is an effective tool for dissent,
computing services and political engagement which has
become regular targets of censorship. In the last few
years, nation-states exerted their control over the
internet to block unwanted links and signals from the
system. A striking example of social networks and
manipulation occurred on twitter when the hackers
collected the data of 25,860 users and performed
malicious related activities. The purpose of this
investigation is to describe the social spam
infrastructure used for political censorship. It is
observed that spam is a common security breach which
is growing very fast and many business communities
are facing this problem in the workplace. In this paper,
the author undertakes an in-depth evaluation of the
spam infrastructure and accounts which facilitated the
cyber-attacks. Moreover, the authors highlighted how
twitter’s relevance based process supported consumers
to reduce the impact of spam and other security issues.
Keywords- social network, Twitter, cyber-attack, spam
and spam infrastructure
INTRODUCTION
In the last few years, the use of social networks has
increased quickly and it is a very effective technique
from the perspective of dissent and political
discussion. There are numerous types of social
networks used in this generation, for example,
Twitter, Facebook, Google+ and so on in order to
interconnect U.S. government with the citizen for
driving consumer’s discourse. It is observed that the
government of the U.K. developed and implement
various strategies for reducing the issue of cyber-
attacks and also ban hackers and other consumers
from social networks [1]. In this research, the author
provides complete information about social spams
and also identify how unknown or third patties
control the political conversations. Moreover, the
researchers determine the accounts and infrastructure
used by hackers for performing hacking-related
processes. The previous investigations identified the
key factors that increase the rate of spam and data
breach but the current research provides in-depth
analysis about the spam infrastructure and networks.
It is observed that attack faced by Twitter involved
around 25,860 fraud accounts used by the hackers for
obtaining data of consumers. In which criminals
performed spam related activities and also transferred
the fraud signals to the networks used by Twitter and
injects 440,793 tweets related to the election. It is
observed that these accounts originated from a pool
of 975,283 accounts and helped the criminals and
third parties for obtaining information related to the
elections [2]. In this paper, the authors explore the
infrastructure system need to reduce such kinds of
cyber-attacks from the system and improve the
security of personal information.
BACKGROUND
In this part, the author evaluates how criminals can
attempt to censor Twitter with the help of fraud
accounts and spams. It is observed that most of the
hackers use the underground markets along with the
malicious system in order to collect the data of
consumers and perform hacking related activities.
The hashtag has emerged on Twitter which is used to
organize communication around the topic and
consumers use such kind of threats to make the
conversation more effective [3]. Because of Hashtags
conversations are susceptible to a particular cyber-
crime and it helps hackers for obtaining the data of
consumers. In the case of cyber-attacks automated
Sybil, accounts increase the rate of conflicts and help
criminals to perform attacks in an effective manner. It
is very complex for consumers to detect the fraud
signals and Sybil accounts from the system due to
which users may lose their personal information and
messages [4]. These strategies involve, analyzing the
social graphs, using backup plans, characterizing the
arrival tome and distribution of the tasks and
recognizing spam and unwanted signals from the
system. For this research, the author relies on
Twitter’s spam and fraud accounts in order to control
and manage cyber-crimes from the system.
METHODOLOGY
Before evaluating and exploring the cyber-attacks the
author describes developed techniques in order to
determine automated accounts which posted
Adapting Social Spam Infrastructure for
Political Censorship

RESEARCH PAPER 2
information related to the Russian election in the year
2011. It is observed that around 46,846 accounts
included in the discussion in which around 25,850 are
defined as fraud accounts developed by the criminals
in order to collect information related to the election
[1]. In order to characterize the cyber-attacks and
issues, the authors first determine all accounts which
posted a tweet including Hastag #триумфальная in
the year 2011. From the previous study it has found
that around 20 Hastags transferred and posted from
46,846 accounts and the below table indicates 10 of
these hashtags and their translation:
(Source: Thomas, Grier and Paxson, 2012)
For determining which accounts were developed by
the hackers, the author relies on internal spam
detection code produced by Twitter that has the
capability for monitoring and controlling the abusive
nature of the consumers. During the analysis of
collected data, the author included the viewpoints of
other experts and also included the findings of
previous papers.
ANALYSIS
In this paper, the author deconstructed the cyber-
attacks into major three sections: the tweets
transferred from the accounts, the registration data of
consumers and the IP addresses which are used by
the criminals for performing data breach. From the
investigation, the author found that the accounts and
details used by the hackers developed for collecting
information related to the election. For spreading
such kinds of information and messages the hackers
produced numbers of fraud accounts with the help of
malicious and spam as a service market. Therefore,
the third parties helped the criminals for producing
numbers of fraud accounts and collected facts
relevant to the election. In order to monitor the facts
and messages transferred by the consumers, the
hackers posted around 440,793 tweets which targeted
almost 20 Hashtags adopted by the consumers.
CONCLUSION
In this paper, the authors analyzed how criminals
developed and implemented spam related activities
and how they adapted numbers of fraud accounts for
collecting information from Twitter. Moreover, the
researchers evaluated a cyber-attack called spam
implemented by the third parties in which criminals
sent fraud messages and tweets from 25,860
accounts. Therefore, it is stated that the adopted
methodologies and data helped the authors for
analyzing the spam infrastructure and processes used
by the hackers for obtaining information related to
the election. Moreover, the authors also provided
several strategies in order to control and manage such
kinds of attacks from the system and protecting data
of consumers from hackers.
REFERENCES
[1]. K. Thomas, C. Grier and, V., Paxson,
“Adapting social spam infrastructure for
political censorship,” In Presented as part of
the 5th {USENIX} Workshop on Large-Scale
Exploits and Emergent Threats, vol. 6, no. 2,
pp. 4-7, 2012.
[2]. G. Danezis and, P., Mittal, “Sybilinfer:
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networks,” In NDSS, vol. 6, no. 2, pp. 1-15,
2009.
[3]. H., Gao, J., Hu, C., Wilson, Z., Li, Y. Chen
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In Proceedings of the 10th ACM SIGCOMM
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[4]. G., Wang, C., Wilson, X., Zhao, Y., Zhu,
M., Mohanlal, H. Zheng and, B.Y., Zhao,
“Serf and turf: crowdturfing for fun and
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