About: Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example

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seeAlso	HTML Summary of #66861 Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example
sameAs	Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example
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dc:hasVersion	Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example (PDF)
Title	Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-07-18
Creator	Çağrı B. Aslan Rahime Belen Sağlam Shujun Li
status	peer reviewed published
Publisher	ACM
abstract	Recent studies have revealed that cyber criminals tend to exchange knowledge about cyber attacks in online social networks (OSNs). Cyber security experts are another set of information providers on OSNs who frequently share information about cyber security incidents and their personal opinions and analyses. Therefore, in order to improve our knowledge about evolving cyber attacks and the underlying human behavior for different purposes (e.g., crime investigation, understanding career development and business models of cyber criminals and cyber security professionals, prediction and prevention of impeding cyber attacks), it will be very useful to detect cyber security related accounts on OSNs automatically, and monitor their activities. This paper reports our preliminary work on automatic detection of cyber security related accounts on OSNs using Twitter as an example, which may allow us to discover unknown cyber security experts and cyber criminals for monitoring purposes. Three machine learning based classification algorithms were applied and compared: decision trees, random forests, and SVM (support vector machines). Experimental results showed that both decision trees and random forests had performed well with an overall accuracy over 95%, and when random forests were used with behavioral features the accuracy had reached as high as 97.877%.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence QA 75 Electronic computers. Computer science TK5105.875.I57 Internet TK5105.888 World Wide Web
list of authors	https://kar.kent.ac.uk/id/eprint/66861#authors
presented at	9th International Conference on Social Media & Society (SM&Society 2017)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #66861 Automatic Detection of Cyber Security Related Accounts on Online Social Networks: Twitter as an example

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