Understanding and Combating Online Social Deception

In today's world, online communication through social network services (SNSs) has become an essential aspect of people's daily lives. As social networking sites (SNSs) have become more sophisticated, cyber attackers have found ways to exploit them for harmful activities such as financial fraud, privacy violations, and sexual or labor exploitation. Thus, it is imperative to gain an understanding of these activities and develop effective countermeasures to build SNSs that can be trusted. The existing approaches have focused on discussing detection mechanisms for a particular type of online social deception (OSD) using various artificial intelligence (AI) techniques, including machine/deep learning (ML/DL) or text mining. However, fewer studies exist on the prevention and response (or mitigation) mechanisms for effective defense against OSD attacks. Further, there have been insufficient efforts to investigate the underlying intents and tactics of those OSD attackers through their in-depth understanding. This dissertation is motivated to take defense approaches to combat OSD attacks through the in-depth understanding of the psychological-social behaviors of attackers and potential victims, which can effectively guide us to take more proactive action against OSD attacks which can minimize potential damages to the potential victims as well as be cost-effective by minimizing or saving recovery cost. In this dissertation, we examine the OSD attacks mainly through two tasks, including understanding their causes and combating them in terms of prevention, detection, and mitigation. In the OSD understanding task, we investigate the intent and tactics of false informers (e.g., fake news spreaders) in propagating fake news or false information. We understand false informers' intent more accurately based on intent-related phrases from fake news contexts to decide on effective and efficient defenses (or interventions) against them. In the OSD combating task, we develop the defense systems following two sub-tasks: (1) The social capital-based friending recommendation system to guide OSN users to choose trustworthy users to defend against phishing attackers proactively; and (2) The defensive opinion update framework for OSN users to process their opinions by filtering out false information. The schemes proposed for combating OSD attacks contribute to the prevention, detection, and mitigation of OSD attacks.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Guo, Zhen

Chair dc:contributor.committeechair

• Cho, Jin-Hee

Committee members dc:contributor.committeemember

• Chen, Ing Ray
• Sengupta, Srijan
• Luther, Kurt
• Lu, Chang Tien

• online social deception
• security
• defense
• detection
• phishing attacks
• social capital
• friending decision
• disinformation
• game theory
• subjective opinion
• uncertainty
• deep reinforcement learning

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:36894

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/114900