TY - GEN
T1 - ACM KDD AI4Cyber
T2 - 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
AU - Samtani, Sagar
AU - Yang, Shanchieh
AU - Chen, Hsinchun
N1 - Funding Information:
The workshop organizers have extensive expertise in numerous AI for Cybersecurity analytics related topics and also serve in key leadership roles within the broader AI for Cybersecurity discipline. A brief biography of each member is summarized 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 40+ articles at MIS Quarterly, Journal
Funding Information:
of MIS, ACM TOPS, IEEE S&P, IEEE ICDM, and others. He has served as Program Chair at IEEE ISI 2020, and PC member at ACM CCS, IEEE S&P and other AI for Cybersecurity venues. He is a member of ACM and IEEE. Dr. Shanchieh (Jay) Yang is a Professor in Computer Engineering and the Director of Global Outreach for 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 over 20 sponsored research projects supported by NSF, IARPA, DARPA, NSA, AFRL, ONR, and ARO. His team has developed several prototypes, including ASSERT to continuously learn and generate emerging statistical attack models, CASCADES to simulate synthetic attack scenarios, and CAPTURE to forecast cyberattacks using unconventional signals in the public domain. He has published more than 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 from funding agencies such as the NSF, DoJ, DHS, and others. As director of the AZSecure Cybersecurity program at UArizona, Dr. Chen has received over $10M from the NSF SFS, SaTC, and CICI programs since 2013. Dr. Chen has published over 900 papers in highly visible IEEE, ACM, and information systems journals and conferences. He is also the founding conference chair for several leading security informatics conferences and workshops. He is a Fellow of the IEEE, ACM, and AAAS.
Funding Information:
This workshop is based upon work funded by DGE-2038483 (SaTC-EDU), OAC-1917117 (CICI), and CNS-1850362 (CRII SaTC). We would like to thank all of the authors for their interest and contributions to this workshop. We would also like to thank each Program Committee Member for their tireless efforts reviewing and providing thoughtful comments for the submitted papers. Finally, we would like to thank the ACM KDD 2021 Workshop Chairs, Dr. Beibei Li (Heinz College at Carnegie Mellon University) and Dr. Lauren Rhue (Robert H. Smith School of Business at University of Maryland) for their advice.
Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/8/14
Y1 - 2021/8/14
N2 - Despite significant contributions to various aspects of cybersecurity, cyber-attacks remain on the unfortunate rise. Increasingly, internationally recognized entities such as the National Science Foundation and National Science & Technology Council have noted Artificial Intelligence can help analyze billions of log files, Dark Web data, malware, and other data sources to help execute fundamental cybersecurity tasks. Our objective for the 1st Workshop on Artificial Intelligence-enabled Cybersecurity Analytics (half-day; co-located with ACM KDD) was to gather academic and practitioners to contribute recent work pertaining to AI-enabled cybersecurity analytics. We composed an outstanding, inter-disciplinary Program Committee with significant expertise in various aspects of AI-enabled Cybersecurity Analytics to evaluate the submitted work. Significant contributions to the half-day workshop were made in the areas of CTI, vulnerability assessment, and malware analysis.
AB - Despite significant contributions to various aspects of cybersecurity, cyber-attacks remain on the unfortunate rise. Increasingly, internationally recognized entities such as the National Science Foundation and National Science & Technology Council have noted Artificial Intelligence can help analyze billions of log files, Dark Web data, malware, and other data sources to help execute fundamental cybersecurity tasks. Our objective for the 1st Workshop on Artificial Intelligence-enabled Cybersecurity Analytics (half-day; co-located with ACM KDD) was to gather academic and practitioners to contribute recent work pertaining to AI-enabled cybersecurity analytics. We composed an outstanding, inter-disciplinary Program Committee with significant expertise in various aspects of AI-enabled Cybersecurity Analytics to evaluate the submitted work. Significant contributions to the half-day workshop were made in the areas of CTI, vulnerability assessment, and malware analysis.
KW - analytics
KW - artificial intelligence
KW - cybersecurity
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85114957618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114957618&partnerID=8YFLogxK
U2 - 10.1145/3447548.3469450
DO - 10.1145/3447548.3469450
M3 - Conference contribution
AN - SCOPUS:85114957618
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 4153
EP - 4154
BT - KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 14 August 2021 through 18 August 2021
ER -