PAPERS
To return to the "Published Papers" main page, click here.
THE TENTH INTERNATIONAL CONFERENCE ON FORENSIC COMPUTER SCIENCE AND CYBER LAW - ICoFCS 2018
Print ISBN 978-85-65069-15-1, pages 43-49
DOI: 10.5769/C2018005 and http://dx.doi.org/10.5769/C2018005
Detecção de Cibercrime em Redes Sociais: Machine Learning
By Jackson Mallmann, Alex dos Santos Xavier, Altair Olivo Santin
To download this paper, click here.
Print ISBN 978-85-65069-15-1, pages 43-49
DOI: 10.5769/C2018005 and http://dx.doi.org/10.5769/C2018005
Detecção de Cibercrime em Redes Sociais: Machine Learning
By Jackson Mallmann, Alex dos Santos Xavier, Altair Olivo Santin
To download this paper, click here.
ABSTRACT
The proposed article presents an academic work which uses Machine Learning techniques to perform detection of cybercrime in messages posted in online social networks, based on tweets datasets. Through a pre-processed step to build a dictionary and using WEKA software, was applied the classify and cluster techniques (K-means, SVM, DT and NB) to detect cybercrimes. The experimental results showed that the use of Machine Learning was essential to the success of this work, where SVM classifier produced 98.77% of accuracy in detection.
KEYWORDS
Machine Learning; Cybercrime; Social Networks; Cluster; Classify.
The proposed article presents an academic work which uses Machine Learning techniques to perform detection of cybercrime in messages posted in online social networks, based on tweets datasets. Through a pre-processed step to build a dictionary and using WEKA software, was applied the classify and cluster techniques (K-means, SVM, DT and NB) to detect cybercrimes. The experimental results showed that the use of Machine Learning was essential to the success of this work, where SVM classifier produced 98.77% of accuracy in detection.
KEYWORDS
Machine Learning; Cybercrime; Social Networks; Cluster; Classify.
