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Attackers' Intention and Influence A...

Lai, Chun-Ming.

Attackers' Intention and Influence Analysis in Social Media.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Attackers' Intention and Influence Analysis in Social Media.
Author:	Lai, Chun-Ming.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2019
Description:	95 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Notes:	Advisor: Wu, S. Felix.
Contained By:	Dissertations Abstracts International81-04A.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13862994
ISBN:	9781085795845

Attackers' Intention and Influence Analysis in Social Media.
Lai, Chun-Ming.

Attackers' Intention and Influence Analysis in Social Media. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 95 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: A.

Thesis (Ph.D.)--University of California, Davis, 2019.

This item must not be sold to any third party vendors.

Human communication behavior has changed significantly with the popularity of Online Social Networks (OSNs). In the absence of access control mechanisms, any user can contribute to an OSNs thread. Individuals can exploit this characteristic to execute targeted attacks, which increases the potential for subsequent malicious behaviors such as phishing, hate speech, malware distribution and fake news propagation. Actually, new patterns of attacking strategies have been discovered, trying to influence OSNs users online and offline behavior implicitly and explicitly.In this dissertation, we will use the concepts from criminology to study attackers strategies on OSNs. Our examinations include: (1) The suitable targets problem studies the characteristic of those post threads embedded with malicious URLs. (2) Effectiveness and influence analysis quantify the reactions and feedbacks for malicious campaigns. (3) Attackers daily and weekly behavior vectors provide the other way to analyze how malicious accounts differ from normal ones. We apply all above problems into engineering framework consideration, aiming at designing a general predictive systems on large-scale OSNs datasets with state-of-the-art machine learning algorithms.To evaluate our model, we will use discussion threads from Facebook public pages. By our research, with limited resource, attackers are able to perform more intelligent and effective malicious campaigns. On the other hand, defenders can focus on suspected targets by certain temporal and spatial variables. It is our hope that the data and analyses presented in this proposal will support a better understanding of attacker strategies and footprints, thereby assisting digital forensics.

ISBN: 9781085795845Subjects--Topical Terms:

199325
Computer science.

Attackers' Intention and Influence Analysis in Social Media.
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520 $a Human communication behavior has changed significantly with the popularity of Online Social Networks (OSNs). In the absence of access control mechanisms, any user can contribute to an OSNs thread. Individuals can exploit this characteristic to execute targeted attacks, which increases the potential for subsequent malicious behaviors such as phishing, hate speech, malware distribution and fake news propagation. Actually, new patterns of attacking strategies have been discovered, trying to influence OSNs users online and offline behavior implicitly and explicitly.In this dissertation, we will use the concepts from criminology to study attackers strategies on OSNs. Our examinations include: (1) The suitable targets problem studies the characteristic of those post threads embedded with malicious URLs. (2) Effectiveness and influence analysis quantify the reactions and feedbacks for malicious campaigns. (3) Attackers daily and weekly behavior vectors provide the other way to analyze how malicious accounts differ from normal ones. We apply all above problems into engineering framework consideration, aiming at designing a general predictive systems on large-scale OSNs datasets with state-of-the-art machine learning algorithms.To evaluate our model, we will use discussion threads from Facebook public pages. By our research, with limited resource, attackers are able to perform more intelligent and effective malicious campaigns. On the other hand, defenders can focus on suspected targets by certain temporal and spatial variables. It is our hope that the data and analyses presented in this proposal will support a better understanding of attacker strategies and footprints, thereby assisting digital forensics.
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