Yuval Elovici

Senior Academic

Content-based detection of terrorists browsing the web using an Advanced Terror Detection System (ATDS)

Yuval Elovici,Bracha Shapira,Mark Last, Omer Zaafrany, Menahem Friedman, Moti Schneider, Abraham Kandel

The Terrorist Detection System (TDS) is aimed at tracking down suspected terrorists by analyzing the content of information they access. TDS operates in two modes: a training mode and a detection mode. During the training mode TDS is provided with Web pages accessed by a normal group of users and computes their typical interests. During the detection mode TDS performs real-time monitoring of the traffic emanating from the monitored group of users, analyzes the content of the Web pages accessed, and issues an alarm if the access information is not within the typical interests of the group. In this paper we present an advanced version of TDS (ATDS), where the detection algorithm was enhanced to improve the performance of the basic TDS system. ATDS was implemented and evaluated in a network environment of 38 users comparing it to the performance of the basic TDS. Behavior of suspected terrorists was simulated by accessing terror related sites. The evaluation included also sensitivity analysis aimed at calibrating the settings of ATDS parameters to maximize its performance. Results are encouraging. ATDS outperformed TDS significantly and was able to reach very high detection rates when optimally tuned.

Publication language English
Pages 244-255
Journal Lecture Notes in Computer Science
Volume 3495
Publication status Published - 01.01.2005

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science
Access to Document
10.1007/11427995_20
Other files and links
Link to publication in Scopus