[Eecs_mscs] EECS Research Seminar Talk-AI-Driven Detection and Defense Against Abusive Friend Attacks in Online Social Networks

    Hunter, Tiffany huntert1 at ohio.edu
    Tue Apr 9 14:28:43 EDT 2024 
    Title: AI-Driven Detection and Defense Against Abusive Friend Attacks in Online Social Networks


[cid: image002.jpg at 01DA8A8A.380347F0 
]Abstract:
Links between people in Online Social Networks (OSNs) have been used by attackers to access private and sensitive user data, post false or abusive information on the timelines and news feeds of victims, scam and influence the perceptions of victims, and spread fake news, misinformation, and malware. A key conduit for these attacks is the abuse of OSN friendships, a breach of trust that is alarmingly common yet often overlooked. Abusive Friend attacks in OSNs manifest primarily in two forms. The first is from existing friends within the network, who may exploit the trust inherent in the friendship. The second type of attack, and perhaps more insidious, originates from those who wish to be friends - those with pending friend requests or creators of fake profiles. The massive growth of social media usage has incentivized perpetrators to connect users through fake "sockpuppet" accounts, an attack that is often successful due to the lack of adequate verification of declared information. The challenge is not only technical but also deeply rooted in the social and psychological dynamics of OSNs. In addressing the complex and multifaceted problem of friend abuse in OSNs, my research, strategically divided into five thrusts, seeks to develop advanced AI-driven solutions and intervention strategies to identify and mitigate the risks associated each specific aspect of this pervasive issue, striving for a secure, more trustworthy online cyberspace. These new generation of Friend Attacks prevention solutions have the potential to help protect millions of vulnerable OSN users from adversaries, sexual predators, online fraudsters, scammers, and malicious hackers.

Speaker Bio:
Dr. Sajedul Talukder is an Assistant Professor of Computer Science at the School of Computing at Southern Illinois University and the founding director of Security and Privacy Enhanced Machine Learning (SUPREME) Lab. He is also a member of the NSF IUCRC Center for Intelligent, Distributed, Embedded Applications and Systems (IDEAS) and Center for Research and Education in AI and Cybersecurity (CARE-AI-C). His current research interests span the areas of security, privacy and distributed systems empowered by machine learning models and privacy-enhancing technologies. He also investigates security and privacy issues in online and geosocial networks, with a focus on leveraging differential privacy, deep learning and federated learning to address complex societal challenges. Dr. Talukder's research has been published in reputed journals and top CS conferences including ACM TSC, Nature Scientific Reports, ANS Transactions, ACM SAC, ICWSM, ACM ASONAM, ACM CHI, ACM WebSci, and has received media attention from many news outlets including prominent national and international outlets. He is serving on the editorial boards and program committees in numerous prestigious conferences and journals including KSII Transactions on Internet and Information Systems, IEEE COMPSAC, ASIACCS, EAI SecureComm, Big Data and Cognitive Computing, ACM CCSC, and ACL ALW. Widely cited in books and research papers, his research has been funded by leading federal agencies like National Science Foundation (NSF), Cyber Florida, Florida Center for Cybersecurity, etc. He is the recipient of the prestigious 2022 NSF CISE CRII award by the Directorate for Computer and Information Science and Engineering (CISE) under the Secure and Trustworthy Cyberspace (SaTC) program. He is also the recipient of the 2022 Illinois Manufacturing Excellence Center (IMEC) award. He is the nominee of the PennWest's Scholar of the Year in 2020 and SIU's University-Level Early Career Faculty Excellence Award in 2022. Dr. Talukder had mentored over thirty students, including K-12 high school teachers, undergraduate students, and graduate students in several programs, such as Science without Borders's Summer Research Program, NSF's Research Experience for Undergraduates (REU), and NSF's Research Experience for Teachers (RET).

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