Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/101875
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Barmpatsalou, Konstantia | - |
dc.contributor.author | Cruz, Tiago | - |
dc.contributor.author | Monteiro, Edmundo | - |
dc.contributor.author | Simões, Paulo | - |
dc.date.accessioned | 2022-09-20T08:11:34Z | - |
dc.date.available | 2022-09-20T08:11:34Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 2169-3536 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/101875 | - |
dc.description.abstract | Culprits' identi cation by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of the mobile forensic data analysis. When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behavior is a rather achievable task. However, when human behavior is involved, such as in cases of traditional crimes, prediction and detection become more compelling. This paper introduces a combined criminal pro ling and suspicious pattern detection methodology for two criminal activities with moderate to the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset for additional testing and veri cation. | pt |
dc.language.iso | eng | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Mobile forensics | pt |
dc.subject | evidence data analysis | pt |
dc.subject | criminal pro ling | pt |
dc.subject | behavioral evidence analysis | pt |
dc.subject | neural networks | pt |
dc.subject | ANFIS | pt |
dc.title | Mobile Forensic Data Analysis: Suspicious Pattern Detection in Mobile Evidence | pt |
dc.type | article | - |
degois.publication.firstPage | 59705 | pt |
degois.publication.lastPage | 59727 | pt |
degois.publication.title | IEEE Access | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.1109/ACCESS.2018.2875068 | pt |
degois.publication.volume | 6 | pt |
dc.date.embargo | 2018-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairetype | article | - |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0002-1097-7742 | - |
crisitem.author.orcid | 0000-0001-9278-6503 | - |
crisitem.author.orcid | 0000-0003-1615-2925 | - |
crisitem.author.orcid | 0000-0002-5079-8327 | - |
Appears in Collections: | I&D CISUC - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Mobile_Forensic_Data_Analysis_Suspicious_Pattern_Detection_in_Mobile_Evidence (1).pdf | 1.97 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
8
checked on Nov 17, 2022
WEB OF SCIENCETM
Citations
5
checked on Nov 15, 2022
Page view(s)
73
checked on Jul 17, 2024
Download(s)
136
checked on Jul 17, 2024
Google ScholarTM
Check
Altmetric
Altmetric
This item is licensed under a Creative Commons License