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Automated Inference and Forecasting for National Security and Cybersecurity

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2016 ORAU Annual Meeting of the Council of Sponsoring Institutions
T. Charles Clancy, PhD
Director, Hume Center

Published in: Government & Nonprofit
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Automated Inference and Forecasting for National Security and Cybersecurity

  1. 1. Institute for Critical Technology and Applied Science hume@vt.edu www.hume.vt.edu Automated Inference and Forecasting for National Security and Cybersecurity T. Charles Clancy, PhD Director, Hume Center 3/15/2016 Inference and Forecasting
  2. 2. Fundamental Shift 3/15/2016 Inference and Forecasting 2
  3. 3. Need for Autonomy Current System Model Emerging System Model 3/15/2016 Inference and Forecasting 3 Analyst (in the loop) Intelligence Platform Typical Delay 1 hour Analyst (on the loop) Intelligence Platform Target Delay <1 second Machine Intelligence
  4. 4. Range of Machine Intelligence 3/15/2016 Inference and Forecasting 4 “The Boom” Forecasting Inference Anomaly Detection Real-Time Response Automated Forensics Anticipatory Analytics Real-Time Analytics Machine Learning
  5. 5. Forecasting Work at Virginia Tech • EMBERS • IARPA-funded effort to forecast social events • Political instability, riots, protests, and election results • Financial instability, market crashes • Disease outbreak and impact • Forecasting events 9 days before the news with precision/recall greater than 0.80 • SIGINT-based Anticipation of Future Events (SAFE) • IARPA follow-on effort to forecast national security events • Integrate classified NSA metadata into the processing engine • Rearchitect processing core to operate within NSA MachineShop cloud • Program kicking off next month 3/15/2016 Inference and Forecasting 5 Archives Caches Selection - Fuse and select predictions - Deliver warnings Enrichment - Tokenization - Entity extraction - Date normalize - Geocoding Open sources Ingest - Read feeds - Convert to JSON - Add identifiers Modeling - Surrogate generation - Prediction generation
  6. 6. Forecasting for Cybersecurity • NIST Cybersecurity Framework is working to push more capability “left of the boom” in a closed-loop cycle • VT is working to adapt the framework to support cyber defense • Recent results have shown ability to forecast grid instability through use of PMU data • Wide range of sensor inputs • Dark web data • STIX/TAXII data feeds • Network IDS information • Malware Analyses 3/15/2016 Inference and Forecasting 6
  7. 7. Deep Learning • Fundamentally transforming image processing • Neural networks that rely on deep networks • Virginia Tech applying to wide range of classical problems • Event detection • Signal classification • Applications in new areas • Naïve learning to process information • Signal processing • Others have applied it to Python interpretation 3/15/2016 Inference and Forecasting 7
  8. 8. Probabilistic Graph Models • The world is full of complex systems generating noisy observations • We seek to infer the behavior and current state of those systems based on observations • Probabilistic graph models are a generalization of Bayesian networks and Markov networks • VT is applying to wide range of challenges • Inferring national security events • Predicting behavior of enemy weapons systems • Identifying relationships between cyber observables • General goal: get inside adversary’s OODA loop 3/15/2016 Inference and Forecasting 8

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