CyberSignature: A user authentication tool based on behavioural biometrics

Nonso Nnamoko; Ioannis Korkontzelos; Joseph Barrowclough; Mark Liptrott

doi:10.1016/j.simpa.2022.100443

CyberSignature: A user authentication tool based on behavioural biometrics

Nonso Nnamoko, Ioannis Korkontzelos, Joseph Barrowclough, Mark Liptrott

Research output: Contribution to journal › Article (journal) › peer-review

2 Citations (Scopus)

73 Downloads (Pure)

Abstract

Behavioural biometrics, such as the way people type on computer keyboard and/or move the cursor are almost impossible to steal. This paper presents CyberSignature1, a tool that uses behavioural biometrics to create unique digital identities that can be used during online card transactions to distinguish legitimate users from fraudsters. The tool is implemented in Python, with a machine learning algorithm at its core. It receives user input data entries from a graphical user interface, similar to an online payment form, and transforms them into unique digital identities. The tool is freely available on Github and is entitled ‘CyberSignature’.

Original language	English
Article number	100443
Journal	Software Impacts
Volume	14
Early online date	15 Nov 2022
DOIs	https://doi.org/10.1016/j.simpa.2022.100443
Publication status	Published - 23 Nov 2022

Keywords

Behavioural biometrics
Payment authentication
Digital identity
Cybersecurity
Identity fraud detection
machine learning

Research Centres

Data and Complex Systems Research Centre

Research Groups

SustainNET

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.simpa.2022.100443

1-s2.0-S2665963822001270-mainFinal published version, 738 KBLicence: CC BY

Cite this

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title = "CyberSignature: A user authentication tool based on behavioural biometrics",

abstract = "Behavioural biometrics, such as the way people type on computer keyboard and/or move the cursor are almost impossible to steal. This paper presents CyberSignature1, a tool that uses behavioural biometrics to create unique digital identities that can be used during online card transactions to distinguish legitimate users from fraudsters. The tool is implemented in Python, with a machine learning algorithm at its core. It receives user input data entries from a graphical user interface, similar to an online payment form, and transforms them into unique digital identities. The tool is freely available on Github and is entitled {\textquoteleft}CyberSignature{\textquoteright}.",

keywords = "Behavioural biometrics, Payment authentication, Digital identity, Cybersecurity, Identity fraud detection, machine learning",

author = "Nonso Nnamoko and Ioannis Korkontzelos and Joseph Barrowclough and Mark Liptrott",

year = "2022",

month = nov,

day = "23",

doi = "10.1016/j.simpa.2022.100443",

language = "English",

volume = "14",

journal = "Software Impacts",

issn = "2665-9638",

publisher = "Elsevier BV",

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T1 - CyberSignature: A user authentication tool based on behavioural biometrics

AU - Nnamoko, Nonso

AU - Korkontzelos, Ioannis

AU - Barrowclough, Joseph

AU - Liptrott, Mark

PY - 2022/11/23

Y1 - 2022/11/23

N2 - Behavioural biometrics, such as the way people type on computer keyboard and/or move the cursor are almost impossible to steal. This paper presents CyberSignature1, a tool that uses behavioural biometrics to create unique digital identities that can be used during online card transactions to distinguish legitimate users from fraudsters. The tool is implemented in Python, with a machine learning algorithm at its core. It receives user input data entries from a graphical user interface, similar to an online payment form, and transforms them into unique digital identities. The tool is freely available on Github and is entitled ‘CyberSignature’.

AB - Behavioural biometrics, such as the way people type on computer keyboard and/or move the cursor are almost impossible to steal. This paper presents CyberSignature1, a tool that uses behavioural biometrics to create unique digital identities that can be used during online card transactions to distinguish legitimate users from fraudsters. The tool is implemented in Python, with a machine learning algorithm at its core. It receives user input data entries from a graphical user interface, similar to an online payment form, and transforms them into unique digital identities. The tool is freely available on Github and is entitled ‘CyberSignature’.

KW - Behavioural biometrics

KW - Payment authentication

KW - Digital identity

KW - Cybersecurity

KW - Identity fraud detection

KW - machine learning

UR - https://doi.org/10.1016/j.simpa.2022.100443

U2 - 10.1016/j.simpa.2022.100443

DO - 10.1016/j.simpa.2022.100443

M3 - Article (journal)

SN - 2665-9638

VL - 14

JO - Software Impacts

JF - Software Impacts

M1 - 100443

ER -

CyberSignature: A user authentication tool based on behavioural biometrics

Abstract

Keywords

Research Centres

Research Groups

UN SDGs

Access to Document

Other files and links

Fingerprint

A behaviour biometrics dataset for user identification and authentication

CyberSignature: Online Authentication using User Behaviour Profiling Phase 2

CyberSignature: Online Authenication using User Behaviour Profiling Phase 1

CyberASAP Demo

Cite this

CyberSignature: A user authentication tool based on behavioural biometrics

Abstract

Keywords

Research Centres

Research Groups

UN SDGs

Access to Document

Other files and links

Fingerprint

Research output

A behaviour biometrics dataset for user identification and authentication

Projects

CyberSignature: Online Authentication using User Behaviour Profiling Phase 2

CyberSignature: Online Authenication using User Behaviour Profiling Phase 1

Activities

CyberASAP Demo

Cite this