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Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern

by Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 52
Year of Publication: 2019
Authors: Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber
10.5120/ijca2019919369

Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber . Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern. International Journal of Computer Applications. 178, 52 ( Sep 2019), 19-30. DOI=10.5120/ijca2019919369

@article{ 10.5120/ijca2019919369,
author = { Eman Osman, Mahmoud Mostafa, Sayed Abdel Gaber },
title = { Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 52 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 19-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number52/30907-2019919369/ },
doi = { 10.5120/ijca2019919369 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:53:49.692517+05:30
%A Eman Osman
%A Mahmoud Mostafa
%A Sayed Abdel Gaber
%T Flexible Malicious Accounts Detector (FMAD) for Mining Twitter Social Network using Features and Accounts Frequent Pattern
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 52
%P 19-30
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Online Social Networks (OSN) have a great role in increasing the communication among people. Their role never stops as they have become the way to share information and the real-time news. However, their unprecedented success has also attracted the attention of hackers, who use OSN to spread spam and malicious contents. Hackers have found a good environment, which is compatible with their goals in terms of widespread reach to the largest number of victims or even spreading large propaganda in a very short time. All this can be done using OSN. The presence of spam and malicious contents on OSN may lead to people’s aversion from these sites. This research tackles this phenomenon by introducing Flexible Malicious Accounts Detector (FMAD) solution, which can detect malicious and spam accounts using predefined features. Additionally, FMDA can identify newly emerging features and classify them as either normal or abnormal. Moreover, FMDA can recognize malicious accounts campaigns. Therefore, the presented solution performs better than all previous approaches that cannot deal with new emerging features. For this purpose, FMAD uses both supervised and unsupervised machine learning techniques. The experiment shows that FMAD results in accuracy reaching 99.75 %.

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Index Terms

Computer Science
Information Sciences

Keywords

OSN Spam Malicious account detection datamining Association rules.

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