Massively Multi-core Acceleration of a Document-Similarity Classifier to Detect Web Attacks

PDF Version Also Available for Download.

Description

This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric to two massively multi-core hardware platforms. The TFIDF classifier is used to detect web attacks in HTTP data. In our parallel hardware approaches, we design streaming, real time classifiers by simplifying the sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. Parallel implementations on the Tilera 64-core System on Chip and the Xilinx Virtex 5-LX FPGA are presented. For the Tilera, we employ a reduced state machine to recognize dictionary terms without ... continued below

Physical Description

PDF-file: 30 pages; size: 0.7 Mbytes

Creation Information

Ulmer, C; Gokhale, M; Top, P; Gallagher, B & Eliassi-Rad, T January 14, 2010.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

This paper describes our approach to adapting a text document similarity classifier based on the Term Frequency Inverse Document Frequency (TFIDF) metric to two massively multi-core hardware platforms. The TFIDF classifier is used to detect web attacks in HTTP data. In our parallel hardware approaches, we design streaming, real time classifiers by simplifying the sequential algorithm and manipulating the classifier's model to allow decision information to be represented compactly. Parallel implementations on the Tilera 64-core System on Chip and the Xilinx Virtex 5-LX FPGA are presented. For the Tilera, we employ a reduced state machine to recognize dictionary terms without requiring explicit tokenization, and achieve throughput of 37MB/s at slightly reduced accuracy. For the FPGA, 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 0.2% of the memory used by 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.

Physical Description

PDF-file: 30 pages; size: 0.7 Mbytes

Source

  • Journal Name: Journal of Parallel and Distributed Computing, vol. 71, no. 2, February 1, 2011, pp. 225-235; Journal Volume: 71; Journal Issue: 2

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Report No.: LLNL-JRNL-422668
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 1020352
  • Archival Resource Key: ark:/67531/metadc836672

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • January 14, 2010

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

Description Last Updated

  • Dec. 6, 2016, 12:47 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 2
Total Uses: 7

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Ulmer, C; Gokhale, M; Top, P; Gallagher, B & Eliassi-Rad, T. Massively Multi-core Acceleration of a Document-Similarity Classifier to Detect Web Attacks, article, January 14, 2010; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc836672/: accessed December 12, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.