dc.contributor.author |
Gamage, P |
|
dc.contributor.author |
Dissanayake, D |
|
dc.contributor.author |
Kumarasinghe, N |
|
dc.contributor.author |
Ganegoda, GU |
|
dc.contributor.editor |
Piyatilake, ITS |
|
dc.contributor.editor |
Thalagala, PD |
|
dc.contributor.editor |
Ganegoda, GU |
|
dc.contributor.editor |
Thanuja, ALARR |
|
dc.contributor.editor |
Dharmarathna, P |
|
dc.date.accessioned |
2024-02-05T13:18:32Z |
|
dc.date.available |
2024-02-05T13:18:32Z |
|
dc.date.issued |
2023-12-07 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/22167 |
|
dc.description.abstract |
Cybercrimes targeting mobile devices are on the
rise, with vishing and smishing attacks being particularly prevalent.
These attacks exploit social engineering techniques to
manipulate individuals into divulging personal information or
engaging in unintended actions. To counter this evolving threat
landscape, this research proposes a pioneering methodology
rooted in voice feature analysis. By distinguishing between human
and robotic voices, this approach aims to discern legitimate calls
from potential scams, thereby mitigating the associated financial
losses and reputational damage. The research delves into the
intricacies of voice feature analysis, leveraging natural language
processing (NLP) and machine learning (ML) techniques to
extract and analyze audio attributes such as pitch, volume, and
temporal patterns. The ultimate objective is to create a binary
classification model that accurately differentiates between human
voice calls and robocalls, incorporating a comprehensive dataset
comprising actual call recordings and synthesized scenarios. This
research advances beyond conventional practices by championing
a holistic analysis of both human and robocalls, contrary to
the prevalent assumption of robocalls exclusively constituting
scams. The application of various audio features, coupled with
nuanced weightage allocation, enhances the model’s discernment
capabilities. The resultant binary classifier is an exemplar of
the innovative fusion of technology and human expertise. In
conclusion, this research introduces a novel dimension to the
combat against vishing and smishing attacks, with a robust voice
feature analysis methodology capable of accurately identifying
human and robotic voices. By effectively distinguishing legitimate
calls from potential threats, this approach presents a promising
avenue for safeguarding individuals and organizations against
the far-reaching consequences of cybercrimes. The comprehensive
analysis, validation, and insights presented in this paper
contribute significantly to the field of cybersecurity and voicebased
communication analysis. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. |
en_US |
dc.subject |
Cyber security |
en_US |
dc.subject |
vishing |
en_US |
dc.subject |
Extraction |
en_US |
dc.subject |
Audio feature analysis |
en_US |
dc.subject |
Classification model |
en_US |
dc.title |
Acoustic signature analysis for distinguishing human vs. synthetic voices in vishing attacks |
en_US |
dc.type |
Conference-Full-text |
en_US |
dc.identifier.faculty |
IT |
en_US |
dc.identifier.department |
Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. |
en_US |
dc.identifier.year |
2023 |
en_US |
dc.identifier.conference |
8th International Conference in Information Technology Research 2023 |
en_US |
dc.identifier.place |
Moratuwa, Sri Lanka |
en_US |
dc.identifier.pgnos |
pp. 1-6 |
en_US |
dc.identifier.proceeding |
Proceedings of the 8th International Conference in Information Technology Research 2023 |
en_US |
dc.identifier.email |
prarthana.18@itfac.mrt.ac.lk |
en_US |
dc.identifier.email |
dushan.18@itfac.mrt.ac.lk |
en_US |
dc.identifier.email |
prasadi.18@itfac.mrt.ac.lk |
en_US |
dc.identifier.email |
upekshag@uom.lk |
en_US |