Intrusion Detection With Support Vector Machines and Generative Models

Abstract

This paper addresses the task of detecting intrusions in the form of malicious attacks on programs running on a host computer system by inspecting the trace of system calls made by these programs. We use 'attack-tree' type generative models for such intrusions to select features that are used by a Support Vector Machine Classifier. Our approach combines the ability of an HMM generative model to handle variable-length strings, i.e. the traces, and the non-asymptotic nature of Support Vector Machines that permits them to work well with small training sets.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2002
Accession Number
ADA439783

Entities

People

  • John Baras
  • Maben Rabi

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Cyber

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Computations
  • Data Sets
  • Detection
  • Generative Models
  • Hidden Markov Models
  • Intrusion
  • Intrusion Detection
  • Intrusion Detectors
  • Kernel Functions
  • Machine Learning
  • Markov Models
  • Models
  • Operating Systems
  • Probability
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Science.
  • Cybersecurity.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML