Biometric Fraud Detection System

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dc.contributor.authorAbdeen, Ahmad
dc.contributor.authorMohanna, Mahmoud
dc.contributor.authorMansur, Yusuf
dc.contributor.authorSonmez, Elena Battini
dc.date.accessioned2026-04-04T18:48:35Z
dc.date.available2026-04-04T18:48:35Z
dc.date.issued2024
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractAs the internet and technology become integral to daily life, user authentication remains a critical challenge, especially with the rise of social engineering. Biometrics, particularly physiological and behavioral, offer promising solutions. Keystroke dynamics, or typing biometrics, identifies users based on their typing patterns. This research evaluates the effectiveness of keystroke dynamics by analyzing existing literature and proposes a fraud detection system combining both physiological and behavioral biometrics. We tested a Vanilla Neural Network (Vanilla-NN) that improves performance in fixed-text keystroke dynamics and propose a novel Long Short-Term Memory (LSTM) attention model for free-text dynamics, delivering promising results. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710778
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207952321
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710778
dc.identifier.urihttps://hdl.handle.net/11411/10243
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260402
dc.subjectBiometric Authentication
dc.subjectDeep Learning
dc.subjectKeystroke Dynamics
dc.subjectLstm Attention Net
dc.subjectNeural Networks
dc.subjectSocial Engineering
dc.titleBiometric Fraud Detection System
dc.typeConference Paper

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