LSU professor receives $1.8 million from NSF for cybersecurity research
An LSU professor could hold the key to successfully tracking or identifying terrorists, serial killers and other threats to homeland and local security.
The National Science Foundation has awarded a $1.8 million grant to Peter Chen, M.J. Foster Distinguished Chair Professor of Computer Science at LSU, for his research on "cybersecurity" methods that could aid law enforcement and security agencies in tracking and capturing terrorists and other types of criminals. The grant marks one of the largest LSU has ever received from the Foundation for pure research.
At the heart of Chen's work are a complex mathematical model and a concept called "smart linkage."
Chen explained that "smart linkage" is simply a method of discovering hidden data relationships and building links based on known or just-discovered relationships between data sets. Some linkages, Chen said, are more important that others. In law enforcement applications, using software and hardware to link existing databases from agencies across the country will allow information to be obtained quickly and easily, he said. Of primary importance in this effort is linking driver's license databases and making driver's licenses swipe-able, so that valid information and photos of individuals could be accessed by airlines, law enforcement officials and other officials.
Chen pointed out that most driver's licenses already come with a "swipe" stripe on the back and that driver's license photos and data are already stored electronically by various state agencies. Thus, the primary problem remaining, he said, is to find the most effective and efficient way of connecting, or linking, these databases.
The smart linkage technology is only one aspect of Chen's research, however. The complex mathematical model he is working on will help law enforcement and security officials cut down on the labor involved in narrowing lists of suspicious individuals. For instance, he explained, using linked databases, the government or police may come up with a list of thousands of suspicious or "problem" individuals. Then, using a computer program based on the mathematical model, the data on the individuals can be crunched and various factors – location, travel patterns, criminal record and so forth – used to narrow down the list to perhaps 1,000 or 100, which is much more manageable for investigators.
The program is based on a recently developed algorithm for a type of database that Chen created and made famous in the 1970s, called the Entity-Relationship Model. The Entity-Relationship Model "reflects relationships that objects and ideas have in the natural world" and serves as the foundation of many systems analysis and design methodologies, computer-aided software engineering and repository systems.
"Right now, if you go to an airport, everyone is searched indiscriminately. If every passenger is forced to waste one hour, that is huge in terms of time and salary," said Chen. "We could use our resources more efficiently by focusing on the ones who are most likely to be terrorists. Our new algorithm, combined with the Entity-relationship model, prioritizes the searches based on different risks from different people."
In early 2001, this mathematical portion of Chen's research earned him a grant of about $417,000 from the Department of Defense. The original focus of this research was to track down hackers.
"If you are looking for a hacker, or signs of hacker activity, you have to deal with millions of computer transactions a day," Chen said. "How do you even begin to sort through this mass of data?"
Chen said that linkage and the mathematical research go hand-in-hand when it comes to advancing the capabilities of law enforcement.
"The data need to be linked and analyzed," he said.
The methods Chen is researching could also be useful for local law enforcement in situations such as a large-scale hunt for a serial killer. The smart linkage would allow law enforcement agencies from different areas to share information more readily and to narrow the search scope to fit particular profiles or patterns, while possibly avoiding some human errors or preconceptions.
Source: Eurekalert & othersLast reviewed: By John M. Grohol, Psy.D. on 21 Feb 2009
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