Cyber-attack Automated Unconventional Sensor Environment (CAUSE)
Cyber-attacks evolve in a phased approach. Detection typically occurs in the later phases of an attack, and analysis often occurs post-mortem to investigate and discover indicators from earlier phases. Observations of earlier attack phases, such as target reconnaissance, planning, and delivery, may enable warning of significant cyber events prior to their most damaging phases.
CAUSE aims to develop and test new automated methods that forecast and detect cyber-attacks significantly earlier than existing methods. The program is envisioned as a multi-year, multi-phase, research effort.
Performers (Prime Contractors)
BAE Systems Information & Electronic System Integration, Inc.; Charles River Analytics, Inc.; Leidos, Inc.; University of Southern California
Related Program(s)
Research Area(s)
- Cybersecurity
- Cyber-event forecasting
- Cyber-actor behavior and cultural understanding
- Threat intelligence
- Threat modeling
- Cyber-event coding
- Cyber-kinetic event detection
Related Publications
To access CAUSE program-related publications, please visit Google Scholar.
Related Article(s)
Machine Learning Can Use Tweets to Spot Critical Security Flaws
Predictive Cybersecurity: Prepare for Attackers Before They’re at Your Door
IARPA Seeks to Predict the Unpredictable
Every Corporation Has An Insider Cyberthreat
Jason Matheny: IARPA to use CAUSE model to detect cyber attacks
Feds seek a cyberattack forecaster
IARPA's new idea to stop cyber threats before they happen
IARPA Solicits For CAUSE Program Forecasting Cyber Attacks
IARPA to Launch Contest on Cyber Attack Prediction; Rob Rahmer Comments
