About: Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90995 Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach
sameAs	Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach
http://eprints.org/ontology/hasAccepted	Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (PDF)
http://eprints.org/ontology/hasDocument	Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (PDF) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (PDF) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (Other)
http://eprints.org/ontology/hasPublished	Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (PDF)
dc:hasVersion	Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (PDF) Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach (PDF)
Title	Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2021-11-08
Creator	Mohamad Imad Mahaini Shujun Li
status	peer reviewed published
Publisher	ACM
abstract	Many cyber security experts, organizations, and cyber criminals are active users on online social networks (OSNs). Therefore, detecting cyber security related accounts on OSNs and monitoring their activities can be very useful for different purposes such as cyber threat intelligence, detecting and preventing cyber attacks and online harms on OSNs, and evaluating the effectiveness of cyber security awareness activities on OSNs. In this paper, we report our work on developing several machine learning based classifiers for detecting cyber security related accounts on Twitter, including a base-line classifier for detecting cyber security related accounts in general, and three sub-classifiers for detecting three subsets of cyber security related accounts (individuals, hackers, and academia). To train and test the classifiers, we followed a more systemic approach (based on a cyber security taxonomy, real-time sampling of tweets, and crowdsourcing) to construct a dataset of cyber security related accounts with multiple tags assigned to each account. For each classifier, we considered a richer set of features than those used in past studies. Among five machine learning models tested, the Random Forest model achieved the best performance: 93% for the base-line classifier, 88-91% for the three sub-classifiers. We also studied feature reduction of the base-line classifier and showed that using just six features we can already achieve the same performance.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q Science
list of authors	https://kar.kent.ac.uk/id/eprint/90995#authors
presented at	The 2nd International Symposium on Foundations of Open Source Intelligence and Security Informatics (FOSINT-SI 2021), co-located with the 13th IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2021)
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90995 Detecting Cyber Security Related Twitter Accounts and Different Sub-Groups: A Multi-Classifier Approach

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