[AISWorld] CFP: The 4th Workshop on Artificial Intelligence-enabled Cybersecurity Analytics (Paper Due 5/21/24)

[Steven Ullman]

Call for Papers and Submission Guidelines

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:

IP reputation services (e.g., blacklisting)
Anomaly and outlier detection
Phishing detection (e.g., email, website, etc.)
Dark Web analytics (e.g., multi-lingual threat detection, key threat actor
identification)
Spam detection
Large-scale and smart vulnerability assessment
Real-time threat detection and categorization
Real-time alert correlation for usable security
Weakly supervised and continual learning for intrusion detection
Adversarial attacks to automated cyber defense
Automated vulnerability remediation
Internet of Things (IoT) analysis (e.g., fingerprinting, measurements,
network telescopes)
Misinformation and disinformation
Deep packet inspection
Static and/or dynamic malware analysis and evasion
Automated mapping of threats to cybersecurity risk management frameworks
Robustifying cyber-defense with deep reinforcement learning or adversarial
learning
Automatic cybersecurity plan or report generation
AI-enabled open-source software security
Analyst-AI interfaces and augmented intelligence for cybersecurity
Large language models for automated threat report generation
Large language models for open source software security
Large language models for adversarial attack (e.g., malware, phishing)
generation and defense
Model verdict explainability in security applications
Privacy preserving security data collection and sharing
Concept drift detection and explanation
Interactive machine learning for security
Few-shot learning for security applications
Resource constrained machine learning
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.

Organizing Team:
• Dr. Sagar Samtani, Indiana University
• Dr. Jay Yang, Rochester Institute of Technology
• Dr. Hsinchun Chen, University of Arizona
• Dr. Benjamin Ampel, Georgia State University
• Dr. Steven Ullman, University of Texas, San Antonio

Key Dates
• Workshop Paper Submission: May 21st, 2024
• Workshop Paper Notification: June 28th, 2024
• Workshop Date: August 25th, 2024

Workshop Homepage: https://ai4cyber-kdd.com/


Submission Site: https://easychair.org/conferences/?conf=ai4cyber0


Steven Ullman
Graduate Research Associate
Artificial Intelligence Laboratory
Management Information Systems
THE UNIVERSITY OF ARIZONA

McClelland Hall, 430
PO Box 210108 | Tucson, AZ 85721

stevenullman at arizona.edu