A configurable-hardware document-similarity classifier to detect web attacks

Abstract

This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric [11] to reconfigurable hardware. The TFIDF classifier is used to detect web attacks in HTTP data. In our reconfigurable hardware approach, we design a streaming, real-time classifier by simplifying an existing sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. We have developed a set of software tools to help automate the process of converting training data to synthesizable hardware and to provide a means of trading off between accuracy and resource utilization. The Xilinx Virtex 5-LX implementation requires two orders of magnitude less memory than the original algorithm. At 166MB/s (80X the software) the hardware implementation is able to achieve Gigabit network throughput at the same accuracy as the original algorithm.