The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA <sup>3</sup> 2024)

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.

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Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3680119
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA 3 2024)

Hanghang Tong, Andrea L. Bertozzi, Jiejun Xu
Adversarial Robustness in Machine Learning
article

The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA 3 2024)

Hanghang Tong, Andrea L. Bertozzi, Jiejun Xu
article en

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.

University of Illinois Urbana-Champaign (US), University of California, Los Angeles (US), HRL Laboratories (United States) (US)
Openalex Percentile: Top 84%
Adversarial Robustness in Machine Learning
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The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA <sup>3</sup> 2024) — Hanghang Tong, Andrea L. Bertozzi, et al. · (2024) | TGRS Research Map | TGRS