Artificial Intelligence (AI)-enabled Cybersecurity Analytics
Topics
This workshop aims to being to gather academic and practitioners to share, disseminate, and communicate completed research papers, work in progress, and review articles pertaining to AI-enabled cybersecurity analytics. Areas of interest include, but are not limited to:Static and/or dynamic malware analysis and evasion
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
Automated mapping of threats to cybersecurity risk management framework
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 culture of data/code sharing in this quickly developing discipline.
Publication
AI4Cyber-KDD proceedings will be published in the ACM Digital Library.