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dc.contributor.authorKantartopoulos, Panagiotis
dc.contributor.authorPitropakis, Nikolaos
dc.contributor.authorMylonas, Alexios
dc.contributor.authorKylilis, Nicolas
dc.date.accessioned2021-02-09T00:08:44Z
dc.date.available2021-02-09T00:08:44Z
dc.date.issued2020-11-06
dc.identifier.citationKantartopoulos , P , Pitropakis , N , Mylonas , A & Kylilis , N 2020 , ' Exploring Adversarial Attacks and Defences for Fake Twitter Account Detection ' , Technologies , vol. 8 , no. 4 , pp. 64 . https://doi.org/10.3390/technologies8040064
dc.identifier.otherPURE: 24569290
dc.identifier.otherPURE UUID: 62118b10-46b4-442b-aa00-2ccc7010b390
dc.identifier.otherBibtex: kantartopoulos2020exploring
dc.identifier.otherORCID: /0000-0001-8819-5831/work/88680243
dc.identifier.urihttp://hdl.handle.net/2299/23875
dc.description.abstractSocial media has become very popular and important in people’s lives, as personal ideas, beliefs and opinions are expressed and shared through them. Unfortunately, social networks, and specifically Twitter, suffer from massive existence and perpetual creation of fake users. Their goal is to deceive other users employing various methods, or even create a stream of fake news and opinions in order to influence an idea upon a specific subject, thus impairing the platform’s integrity. As such, machine learning techniques have been widely used in social networks to address this type of threat by automatically identifying fake accounts. Nonetheless, threat actors update their arsenal and launch a range of sophisticated attacks to undermine this detection procedure, either during the training or test phase, rendering machine learning algorithms vulnerable to adversarial attacks. Our work examines the propagation of adversarial attacks in machine learning based detection for fake Twitter accounts, which is based on AdaBoost. Moreover, we propose and evaluate the use of k-NN as a countermeasure to remedy the effects of the adversarial attacks that we have implemented.en
dc.format.extent1
dc.language.isoeng
dc.relation.ispartofTechnologies
dc.titleExploring Adversarial Attacks and Defences for Fake Twitter Account Detectionen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.3390/technologies8040064
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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