Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6111
Title: A New Cyber Security Alert System for Twitter
Authors: Erkal, Yiğit
Sezgin, Mustafa
Gündüz, Sedef
Keywords: Online Social Networks
Cyber Security
Attacks
Detection System
Twitter
Issue Date: 2015
Publisher: Elsevier Science Bv
Source: IEEE 14th International Conference on Machine Learning and Applications ICMLA -- DEC 09-11, 2015 -- Miami, FL
Abstract: This study proposes an autonomous early decision system for cyber security related contents of Twitter. In the context, both cyber and non-cyber security related tweets are collected and the obtained data is trained by means of Naive Bayes Classifier. Besides, Term Frequency - Inverse Document Frequency (TF-IDF) term weighting method is used for vectorization purpose. Experimental results show that, the developed system can classify the tweets in terms of their cyber security related or non-related security with the 70.03% success rate. It can be included that the system can be used as an alert system on Twitter for early cyber-attack detection.
URI: https://doi.org/10.1109/ICMLA.2015.133
https://hdl.handle.net/20.500.11851/6111
ISBN: 978-1-5090-0287-0
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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