Accession Number:

ADA415160

Title:

Categorizing Network Attacks Using Pattern Classification Algorithms

Descriptive Note:

Master's thesis

Corporate Author:

AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING AND MANAGEMENT

Personal Author(s):

Report Date:

2002-03-01

Pagination or Media Count:

115.0

Abstract:

Information systems are often inundated with thousands of attack alerts to distinguish novice hacker probes from genuine threats. Pattern classification can help filter relatively benign attacks from alerts generated by anomaly detectors, limited the numbers of alerts to requiring attention. This research investigates the feasibility of using pattern classification algorithms on network packed header information to classify network attacks. Both liner discrimination and radial basis function algorithms are trained using flood and scan attacks. The classifiers are then tested with unknown floods and scans to determine how well they categorize previously unseen attacks.

Subject Categories:

  • Numerical Mathematics
  • Computer Systems
  • Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE