Briefs

Technology Research News (Oct 30/Nov 6, 2002) writes on "Text software spots intruders"

Researchers from the University of California at Davis have taken an unusual tack in anomaly detection by adapting text classification techniques to intrusion detection. Their initial results suggest that the technique could produce an anomaly detection system with a reasonable error rate.

The idea to apply text classification to intrusion detection began with a conversation about categorizing Web pages into clusters that share a given property, said V. Rao Vemuri, a professor of applied science and computer science at the University of California at Davis, and a scientist at Lawrence Livermore National Laboratories.

Instead of categorizing Web pages, however, the researchers used the classification system to categorize computer users into just two groups -- authorized users and intruders. "The problem is to decide what 'text'" to use for the problem, Vemuri said. We wanted some objective way of characterizing a user that the user... cannot consciously influence" in order to prevent an intruder from fooling the system, he said.

Here is full story from TRN magazine.