Cybercrime detection techniques based on support vector machines

Hanif - Mohaddes Deylami, Yashwant Prasad Singh


This paper presents the cybercrime detection model by using support vector machines (SVMs) to classify social network (Facebook) dataset. We try to compare between three kinds of classification algorithms such as: SVMs, AdaBoostM1, and NaiveBayes in order to find a high percentage of classification accuracy. Finally, we conclude SVMs as the best classification algorithm, which uses different breeds of kernel functions in order to improve the classification accuracy on Facebook dataset. Besides, we are using the Weka 3.7.4 software to evaluate classifiers on Facebook dataset.

Full Text:




  • There are currently no refbacks.

Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

Copyright © Sciedu Press 
To make sure that you can receive messages from us, please add the '' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.