Paper Title :Detection Of Suspicious Urls In Twitter Stream
Author :Nilesh B. Nikumbhe, Ravi Uyyala
Article Citation :Nilesh B. Nikumbhe ,Ravi Uyyala ,
(2014 ) " Detection Of Suspicious Urls In Twitter Stream " ,
International Journal of Advances in Science, Engineering and Technology(IJASEAT) ,
pp. 40-43,
Volume-2,Issue-3
Abstract : This paper presents a survey of twitter stream which is vital for finding twitter attacks. Online social
networking has become a very popular way for users to meet and interact online. Users spend a large amount of time on
popular social network platforms such as Facebook, MySpace, or Twitter, storing and sharing a wealth of personal
information. This information, as well as the possibility of contacting billions of users, also attracts the interest of
cybercriminals. Millions of users tweeting around the world, real time systems and different types of mining tools are
emerging to allow people tracking the events and post on Twitter. Twitter allows users to discuss events and post their
status, these services open opportunities for new forms of spam. Trending topics, the most talked about trending topics
on Twitter at a given point in time, have been seen as an opportunity to generate traffic and revenue. Spammers post
tweets containing typical words of a trending topic and URLs, usually obfuscated by URL that lead users to completely
unrelated websites. So to avoid that presenting an online spam filtering system that can be deployed as a component of
the online social networks platform to inspect messages generated by users in real-time. And that reconstructs spam
messages into campaigns for classification rather than determine them individually. Although campaign identification
has been used for offline spam filtering, apply this technique to aid the online spam detection problem with low
overhead. Accordingly, system adopts a set of features that effectively distinguish and determine spam campaigns. It
drops messages classified as “spam” before they reach the intended destination, thus protecting them from various kinds
of malicious aspects. Firstly collecting a large dataset of Twitter. From that construct a large labeled collection of users,
manually classified into spammers and non-spammers. In this system investigates correlations of URL redirect chains
extracted from several tweets and forms frequently shared url. Because attackers have limited resources and usually they
reuse them. Develop methods to discover correlated URL redirect chains using the frequently shared URLs and to
determine their suspiciousness. collect numerous tweets from the Twitter public timeline and build a statistical classifier
using them. Evaluation results show that classifier accurately and efficiently detects suspicious URLs.
Type : Research paper
Published : Volume-2,Issue-3
DOIONLINE NO - IJASEAT-IRAJ-DOIONLINE-1045
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Copyright: © Institute of Research and Journals
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Published on 2014-07-09 |
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