Topics for discussion may include but are not limited to:

  • Insider threat detection

  • Network and endpoint forensics

  • Governance, compliance and exfiltration detection

  • Detection of script-based and malware-less attacks

  • Automated malware detection and classification

  • Vulnerability assessment

ML techniques and analytic or predictive themes might include:

  • Statistical analysis on large and small datasets

  • Unique considerations of base-rate fallacy for data science in information security

  • Data sources and data exploration and subsequent findings

  • Unique approaches to dataset visualization

  • Unsupervised methods and anomaly detection

  • Adversarial machine learning

  • Original or cross-domain deep learning architectures applied to information security data

  • Natural language processing

  • Reinforcement learning for automating security tasks