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
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Steven Ullman
Assistant Professor
The University of Texas at San Antonio
San Antonio TX
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