TY - GEN
T1 - The 8th Workshop on Graph Techniques for Adversarial Activity Analytics (GTA3 2024)
AU - Xu, Jiejun
AU - Tong, Hanghang
AU - Bertozzi, Andrea
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/10/21
Y1 - 2024/10/21
N2 - 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.
AB - 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.
KW - adversarial activity analytics
KW - graph machine learning
KW - graph mining
KW - knowledge representation
UR - http://www.scopus.com/inward/record.url?scp=85210006059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85210006059&partnerID=8YFLogxK
U2 - 10.1145/3627673.3680119
DO - 10.1145/3627673.3680119
M3 - Conference contribution
AN - SCOPUS:85210006059
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5603
EP - 5604
BT - CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Y2 - 21 October 2024 through 25 October 2024
ER -