Hybrid features for detection of malicious user in YouTube

Social media is any site that provides a network of people with a place to make connections. An example of the media is YouTube that connects people through video sharing. Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-pro...

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Main Author:	Sadoon, Omar Hadeb
Format:	Thesis
Language:	eng
Published:	2017
Subjects:	T58.5-58.64 Information technology
Online Access:	https://etd.uum.edu.my/6562/1/816170_01.pdf
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id	my-uum-etd.6562
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng
advisor	Yusof, Yuhanis
topic	T58.5-58.64 Information technology
spellingShingle	T58.5-58.64 Information technology Sadoon, Omar Hadeb Hybrid features for detection of malicious user in YouTube
description	Social media is any site that provides a network of people with a place to make connections. An example of the media is YouTube that connects people through video sharing. Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-promote their videos or broadcast viruses and malware. Even though detection of malicious users have been done using various features such as the content, user social activity, social network analyses, or hybrid features, the detection rate is still considered low (i.e., 46%). This study proposes a new set of features that includes features of the user, user behaviour and also features created based on Edge Rank concept. The work was realized by analysing a set of YouTube users and their shared video. It was followed by the process of classifying users using 22 classifiers based on the proposed feature set. An evaluation was performed by comparing the classification results of the proposed hybrid features against the non-hybrid ones. The undertaken experiments showed that most of the classifiers obtained better result when using the hybrid features as compared to using the non-hybrid set. The average classification accuracy is at 95.6% for the hybrid feature set. The result indicates that the proposed work would benefit YouTube users as malicious users who are sharing non-relevant content can be detected. The results also lead to the optimization of system resources and the creation of trust among users.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Sadoon, Omar Hadeb
author_facet	Sadoon, Omar Hadeb
author_sort	Sadoon, Omar Hadeb
title	Hybrid features for detection of malicious user in YouTube
title_short	Hybrid features for detection of malicious user in YouTube
title_full	Hybrid features for detection of malicious user in YouTube
title_fullStr	Hybrid features for detection of malicious user in YouTube
title_full_unstemmed	Hybrid features for detection of malicious user in YouTube
title_sort	hybrid features for detection of malicious user in youtube
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2017
url	https://etd.uum.edu.my/6562/1/816170_01.pdf
_version_	1747828092486811648
spelling	my-uum-etd.65622021-08-18T06:40:02Z Hybrid features for detection of malicious user in YouTube 2017 Sadoon, Omar Hadeb Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences T58.5-58.64 Information technology Social media is any site that provides a network of people with a place to make connections. An example of the media is YouTube that connects people through video sharing. Unfortunately, due to the explosive number of users and various content sharing, there exist malicious users who aim to self-promote their videos or broadcast viruses and malware. Even though detection of malicious users have been done using various features such as the content, user social activity, social network analyses, or hybrid features, the detection rate is still considered low (i.e., 46%). This study proposes a new set of features that includes features of the user, user behaviour and also features created based on Edge Rank concept. The work was realized by analysing a set of YouTube users and their shared video. It was followed by the process of classifying users using 22 classifiers based on the proposed feature set. An evaluation was performed by comparing the classification results of the proposed hybrid features against the non-hybrid ones. The undertaken experiments showed that most of the classifiers obtained better result when using the hybrid features as compared to using the non-hybrid set. The average classification accuracy is at 95.6% for the hybrid feature set. The result indicates that the proposed work would benefit YouTube users as malicious users who are sharing non-relevant content can be detected. The results also lead to the optimization of system resources and the creation of trust among users. 2017 Thesis https://etd.uum.edu.my/6562/ https://etd.uum.edu.my/6562/1/816170_01.pdf text eng public masters masters Universiti Utara Malaysia Abdesslem, F. Ben, Parris, I., & Henderson, T. (2012). Reliable online social network data collection. In Computational Social Network: Mining and Visualization (pp. 183-210). http://doi.org/10.10071978-1-4471-4054-2_8 Academia. (2016). About academia.edu. Retrieved January 1, 2016, from http://www.academia.edu/about Acufia, E., & Rodriguez, C. (2004). The Treatment of Missing Values and its Effect on Classifier Accuracy. In Classification, Clustering, and Data Mining Applications (pp. 639-647). incollection, Springer. http://doi.org/1O.10071978-3-642-17103-1_60 Alberto, T. C., Lochter, J. V, & Almeida, T. A. (2015). TubeSpam: Comment Spam Filtering on YouTube. 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Hybrid features for detection of malicious user in YouTube

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