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