The ATDS system is aimed at detecting potential terrorists on the Web by tracking and analyzing the content of pages accessed by users in a known environment (e.g., university, organization). The system would alert and report on any user who is “too” interested in terrorist-related content. The system learns and represents the typical interests of the users in the environment. It then monitors the content of pages the users access and compares it to the typical interests of the users in the environment. The system issues an alarm if it discovers a user whose interests are significantly and consistently dissimilar to the other users' interests. This paper briefly reviews the main ideas of the system and suggests improving the detection accuracy by learning terrorists' typical behaviors from known terrorist related sites. An alarm would be issued only if a “non-typical” user is found to be similar to the typical interests of terrorists. Another enhancement suggested is the analysis of the visual content of the pages in addition to the textual content.
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