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Detecting New Events from Microblogs Using Convolutional Neural Networks Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Detection of Malicious URLs on Twitter

Authors : Nureni Ayofe Azeez, Oluwadamilola Atiku, Sanjay Misra, Adewole Adewumi, Ravin Ahuja, Robertas Damasevicius

Published in: Advances in Electrical and Computer Technologies

Publisher: Springer Singapore

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Abstract

Twitter is an online social network that is popular for its use of 140-character messages called tweets to exchange information, news and connects the global world. Due to the large audience of people that make use of twitter, malicious users from time to time try to find ways to attack it. This is because the usages of URLs in tweets expose them and make them prone to attacks such as malware distribution, phishing, spam and scam. In this project, a system is developed that detected suspicious URLs on twitter, and the proposed system investigates the correlation of URL redirect chains extracted from various tweets on twitter. Therefore, after a large number of tweets are collected from twitter public timeline, a classifier by naive Bayes machine learning algorithm is built using the data.

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Metadata
Title
Detection of Malicious URLs on Twitter
Authors
Nureni Ayofe Azeez
Oluwadamilola Atiku
Sanjay Misra
Adewole Adewumi
Ravin Ahuja
Robertas Damasevicius
Copyright Year
2020
Publisher
Springer Singapore
Book
Advances in Electrical and Computer Technologies

Print ISBN: 978-981-15-5557-2

Electronic ISBN: 978-981-15-5558-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-15-5558-9_29

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