Utilizing Graphics Processing Units for Network Anomaly Detection

Abstract

This research explores the benefits of using commonly-available graphics processing units (GPUs) to perform classification of network traffic using supervised machine learning algorithms. Two full factorial experiments are conducted using a NVIDIA GeForce GTX 280 graphics card. The goal of the first experiment is to create a baseline for the relative performance of the CPU and GPU implementations of artificial neural network (ANN) and support vector machine (SVM) detection methods under varying loads. The goal of the second experiment is to determine the optimal ensemble configuration for classifying processed packet payloads using the GPU anomaly detector. The GPU ANN achieves speedups of 29x over the CPU ANN. The GPU SVM detection method shows training speedups of 85x over the CPU. The GPU ensemble classification system provides accuracies of 99% when classifying network payload traffic, while achieving speedups of 2-15x over the CPU configurations.

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Document Details

Document Type
Technical Report
Publication Date
Sep 13, 2012
Accession Number
ADA568667

Entities

People

  • Jonathan D. Hersack

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Cyber

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata Theory
  • Bayesian Networks
  • Computational Science
  • Computer Languages
  • Computer Networks
  • Computer Programming
  • Computers
  • Data Mining
  • Dimensionality Reduction
  • Information Science
  • Instruction Set Architecture
  • Intrusion Detectors
  • Kernel Functions
  • Machine Learning
  • Network Science
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks