The irreversible dependence on computing technology has paved the way for cybersecurity’s rapid emergence as one of modern society’s grand challenges. To combat the ever-evolving, highly-dynamic threat landscape, numerous academics and industry professionals are systematically searching through billions of log files, social media platforms (e.g., Dark Web), malware files, and other data sources to preemptively identify, mitigate, and remediate emerging threats and key threat actors. Artificial Intelligence (AI)-enabled analytics has started to play a pivotal role in sifting through large quantities of these heterogeneous cybersecurity data to execute fundamental cybersecurity tasks such as asset management, vulnerability prioritization, threat forecasting, and controls allocations. Indeed, the recent advances in AI-enabled analytics techniques such as Large Language Models (LLMs), self-supervised learning, graph neural networks, and others offer ripe opportunities for defenders to enhance their cybersecurity capabilities. To this end, this workshop aims to convene academics and practitioners (from industry and government) to share, disseminate, and communicate completed research papers, work in progress, and review articles about AI-enabled cybersecurity analytics. Areas of interest include, but are not limited to:
Each manuscript must clearly articulate their data (e.g., key metadata, statistical properties, etc.), analytical procedures (e.g., representations, algorithm details, etc.), and evaluation set up and results (e.g., performance metrics, statistical tests, case studies, etc.). Providing these details will help reviewers better assess the novelty, technical quality, and potential impact. Making data, code, and processes publicly available to facilitate scientific reproducibility is not required. However, it is strongly encouraged, as it can help facilitate a data/code sharing culture in this quickly developing discipline.
All submissions must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. Submissions are limited to a 4-page initial submission, excluding references or supplementary materials. Upon acceptance, the authors can include an additional page (5-page total) for that camera-ready version that accounts for reviewer comments. Authors should use supplementary material only for minor details that do not fit in the four pages but enhance the scientific reproducibility of the work (e.g., model parameters). Since all reviews are double-blind, author names and affiliations should NOT be listed. For accepted papers, at least one author must attend the workshop to present the work. Based on the reviews received, accepted papers will be designated as a contributed talk (four total, 15 minutes each) or as a poster. All accepted papers will be posted on the workshop website but will not be included in the KDD Proceedings.
TBD
Submission Site: Easy Chair Submission
Dr. Sagar Samtani
Indiana University
Dr. Jay Yang
Rochester Institute of Technology
Dr. Hsinchun Chen
University of Arizona
Mr. Ben Ampel
Georgia State University
Mr. Steven Ullman
University of Texas, San Antonio