TY - GEN
T1 - ACM KDD AI4Cyber/MLHat
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
AU - Samtani, Sagar
AU - Wang, Gang
AU - Ahmadzadeh, Ali
AU - Ciptadi, Arridhana
AU - Yang, Shanchieh
AU - Chen, Hsinchun
N1 - Funding Information: This workshop is based upon work funded by DGE-2038483 (SaTC-EDU), DGE-1946537 (SFS), and CNS-1850362 (CRII SaTC). We thank the authors for their contributions. We extend our appreciation to We would also like to thank each Program Committee Member for reviewing the submitted papers. Funding Information: The workshop organizers have extensive expertise in numerous AI for Cybersecurity analytics-related topics and lead other highly-visible AI for Cybersecurity initiatives. Each organizer’s biography appears below: • Dr. Sagar Samtani is an Assistant Professor and Grant Thornton Scholar of Operations and Decision Technologies at Indiana University. Dr. Samtani’s research on CTI for Dark Web analytics and scientific cyberinfrastructure security have been funded by the NSF SaTC, CICI, and CRII programs. Dr. Samtani has published 50+ articles at MIS Quarterly, Journal of MIS, ACM TOPS, IEEE S&P, IEEE ICDM, and others. • Dr. Gang Wang is an Assistant Professor of Computer Science at the University of Illinois at Urbana-Champaign. He obtained his Ph.D. from UC Santa Barbara in 2016. His research interests are Security and Privacy, and Data Mining. He is a Funding Information: recipient of the NSF CAREER Award (2018). His projects have been covered by media outlets such as The New York Times, Boston Globe, CNN, and ACM TechNews. Dr. Ali Ahmadzadeh is the head of the Blue Hexagon Labs. Ali leads the effort to develop state-of-the-art cybersecurity threat detection using advanced deep learning. Dr. Ahmadzadeh has more than 20 patents, and he has published in top-tier journals and conferences in communication networks and computer security. He received his Ph.D. from the University of Waterloo. Dr. Arridhana Ciptadi is a Principal Engineer at TruEra. He obtained his Ph.D. in Computer Science from Georgia Tech in 2016. His research interests are deep learning, adversarial machine learning, and cybersecurity. His work has been published in top-tier venues. His projects have been covered by media outlets such as MIT Technology Review. Dr. Shanchieh (Jay) Yang is a Professor in Computer Engineering and the Director of Global Outreach for the Global Cybersecurity Institute at Rochester Institute of Technology. His research focuses on advancing machine learning, modeling, and simulation for predictive cyber intelligence and anticipatory cyber defense. He has worked on 20+ sponsored research projects and has published 70+ peer-reviewed papers. Dr. Hsinchun Chen is a Regents’ Professor of Management Information Systems at the University of Arizona. Dr. Chen is the founder and director of the Artificial Intelligence Lab, an internationally recognized research lab renowned for its research on AI cybersecurity. Dr. Chen has received over $50M of federal funding and has published 900+ papers in highly visible IEEE, ACM, and information systems venues. He is a Fellow of the IEEE, ACM, and AAAS. Publisher Copyright: © 2022 Owner/Author.
PY - 2022/8/14
Y1 - 2022/8/14
N2 - Federal funding agencies and industry entities are seeking innovative approaches to address the ever-growing cybersecurity crisis. Increasingly, numerous cybersecurity thought leaders are indicating that Artificial Intelligence (AI)-enabled analytics can help tackle key cybersecurity tasks and deploy defenses. This half-day workshop, co-located with ACM KDD, sought to attain significant research contributions to various aspects of AI-enabled analytics for cybersecurity applications and deployable defense solutions from academics and practitioners. This workshop was a joint workshop of the 2021 AI-enabled Cybersecurity Analytics and 2021 International Workshop on Deployable Machine Learning for Security Defense. As such, we developed an interdisciplinary Program Committee with significant experience in various aspects of AI, cybersecurity, and/or deployable defense.
AB - Federal funding agencies and industry entities are seeking innovative approaches to address the ever-growing cybersecurity crisis. Increasingly, numerous cybersecurity thought leaders are indicating that Artificial Intelligence (AI)-enabled analytics can help tackle key cybersecurity tasks and deploy defenses. This half-day workshop, co-located with ACM KDD, sought to attain significant research contributions to various aspects of AI-enabled analytics for cybersecurity applications and deployable defense solutions from academics and practitioners. This workshop was a joint workshop of the 2021 AI-enabled Cybersecurity Analytics and 2021 International Workshop on Deployable Machine Learning for Security Defense. As such, we developed an interdisciplinary Program Committee with significant experience in various aspects of AI, cybersecurity, and/or deployable defense.
KW - analytics
KW - artificial intelligence
KW - cybersecurity
KW - deployable defense
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85137146057&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137146057&partnerID=8YFLogxK
U2 - 10.1145/3534678.3542894
DO - 10.1145/3534678.3542894
M3 - Conference contribution
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 4900
EP - 4901
BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 14 August 2022 through 18 August 2022
ER -