The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA3 2024)

Jiejun Xu, Hanghang Tong, Andrea Bertozzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Graphs are powerful analytic tools for modeling adversarial activities across a wide range of domains and applications. Examples include identifying and responding to cybersecurity systems' threats and vulnerabilities, strengthening critical infrastructure's resilience and robustness, and combating covert illicit activities that span various domains like finance, communication, and transportation. With the rapid development of generative AI, the lifecycle and throughput of adversarial activities, such as generating attacks or synthesizing deceptive signals, have accelerated significantly. For instance, a malicious actor can generate a large number of malware variants to flood defense systems or create agents to disseminate misleading signals, obscuring their activities. Consequently, there is a pressing need for novel and effective technology to autonomously handle these adversarial activities and keep pace with the evolving threats. The purpose of this workshop is to provide a forum to discuss emerging research problems and novel approaches in graph analysis for modeling adversarial activities in the age of generative AI.

Original languageEnglish (US)
Title of host publicationCIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages5603-5604
Number of pages2
ISBN (Electronic)9798400704369
DOIs
StatePublished - Oct 21 2024
Event33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 - Boise, United States
Duration: Oct 21 2024Oct 25 2024

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
ISSN (Print)2155-0751

Conference

Conference33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Country/TerritoryUnited States
CityBoise
Period10/21/2410/25/24

Keywords

  • adversarial activity analytics
  • graph machine learning
  • graph mining
  • knowledge representation

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences

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