Progressive Email Classifier (PEC) for Ingress Enterprise Network Traffic Analysis

Abstract

This report summarizes research findings of the project "Progressive Email Classifier (PEC) for Ingress Enterprise Network Traffic Analysis." We have developed a series of solutions which are designed to serve the needs of gateway level detection of spam like traffic, with and without prior defined patterns. The first major solution is the scoreboard architecture, which can track the scores and ages of patterns with a constant running time. Next, we developed a packetized processing software architecture, PFlex, for the regular expression pattern matcher Flex to support packet level content scanning. The third major solution is a SA2PX tool which can translate SpamAssassin into Posix format, so that it can be ported to different platforms. The fourth major solution is a new Nondeterministic Finite Automata (NFA) algorithm for regular expression scanning, which can support overlapped matching, and can resolve matching ambiguity. We have tested these solutions in simulations, and run them on different computing platforms, including the multicore PC, the Bivio model 7500 DPI multicomputer, and FPGA. The solutions can be integrated into a system to supplement existing server-based spam filters by providing real-time statistics based spam information. The overall system design can be broadly expanded to support other network security functions.

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Document Details

Document Type
Technical Report
Publication Date
Sep 21, 2010
Accession Number
ADA534227

Entities

People

  • Jyh-charn Liu

Organizations

  • Texas Engineering Experiment Station

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Agreements
  • Algorithms
  • Contractors
  • Databases
  • Deep Packet Inspection
  • Department Of Defense
  • Detection
  • Education
  • Engineering
  • Information Operations
  • Mathematics
  • Military Research
  • Numbers
  • Software Design
  • Standards
  • Students

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Computer Vision.
  • Parallel and Distributed Computing.

Technology Areas

  • Cyber