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A memory-efficient deterministic finite automaton-based bit-split string matching scheme using pattern uniqueness in

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This study introduces a memory-efficient string matching scheme for deep packet inspection (DPI). It significantly reduces storage costs by optimizing pattern matching in large datasets.

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Area of Science:

  • Computer Science
  • Network Security
  • Algorithm Design

Background:

  • Deep Packet Inspection (DPI) requires efficient string matching for large pattern sets.
  • Existing bit-split string matching schemes face critical memory limitations with increasing pattern counts.
  • The memory footprint of string matching engines is a bottleneck in high-performance network analysis.

Purpose of the Study:

  • To propose a novel memory-efficient bit-split string matching scheme for DPI.
  • To reduce the memory requirements of string matching engines when handling numerous target patterns.
  • To leverage pattern uniqueness for optimizing deterministic finite automaton (DFA)-based matching.

Main Methods:

  • Developed a pattern grouping technique to extract unique patterns where no pattern is a suffix of another.
  • Applied this pattern grouping to multiple finite-state machine (FSM) tiles in bit-split string matching.
  • Modified the memory-based bit-split string matching engine to store only pattern match indices for unique patterns, eliminating matching vectors.

Main Results:

  • The proposed scheme significantly decreases memory requirements compared to previous methods.
  • Experimental results demonstrate substantial reductions in storage cost.
  • The optimized approach maintains efficient pattern matching capabilities for DPI.

Conclusions:

  • The novel pattern grouping strategy effectively reduces memory usage in bit-split string matching for DPI.
  • This memory-efficient scheme addresses a critical challenge in handling large-scale pattern matching.
  • The findings offer a practical solution for improving the performance and scalability of network security systems.