Sign In

Communications of the ACM

ACM TechNews

Iarpa to Develop Early-Warning System For Cyberattacks

View as: Print Mobile App Share:
Artist's illustration of cyber-computing.

A multi-year research and development initiative launched by the U.S. Intelligence Advanced Research Projects Activity is aimed at creating technologies that could provide early warning of cyberattacks.

Credit: Kacper Pempel/Reuters

The U.S. Intelligence Advanced Research Projects Activity (IARPA) has launched Cyberattack Automated Unconventional Sensor Environment (CAUSE), a multi-year research and development initiative to create new technologies that could provide an early warning system for cyberattacks.

IARPA notes CAUSE could help organizations move past the reactive approach to defending against and responding to cyberattacks. The agency says the three-and-a-half year program will develop software to sense unconventional indicators of a cyberattack, and will use the data to develop models and machine-learning systems that can create probabilistic warnings.

CAUSE includes four main research partners: BAE Systems, Charles River Analytics, Leidos, and the University of Southern California. Each partner has a novel approach to addressing the challenge, says IARPA program manager Robert Rahmer. He notes the researchers apply human behavioral, cyberattack, and social theories to publicly available information with the goal of developing unconventional sensors of activities that indicate the early stages of an attack.

"Signals of interest are derived from examining emotional language and sentiment-related characteristics, analyzing topics of discussion, and looking at technical communications," BAE says. "This differs from traditional cyberattack detection, which utilizes conventional sensors running with private data where the focus is on the detection of an ongoing event, rather than prediction."

From The Wall Street Journal 
View Full Article


Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA


No entries found

Sign In for Full Access
» Forgot Password? » Create an ACM Web Account