Accession Number:

AD1025911

Title:

Intrusion Detection Systems with Live Knowledge System

Descriptive Note:

Technical Report,20 May 2015,19 May 2016

Corporate Author:

UNIVERSITY OF TASMANIA SANDY BAY Australia

Personal Author(s):

Report Date:

2016-05-31

Pagination or Media Count:

10.0

Abstract:

Detecting phishing websites has been noted as a complex and dynamic problem area because of the subjective considerations and ambiguities of detection mechanism. Either machine learning technique or human expert system has been applied to acquire and maintain the knowledge for phishing website detection and prediction but neither did work successfully. In this project, we propose novel approach that uses Ripple-down Rule RDR to maintain the knowledge from human experts with knowledge base generated by the Induct RDR, which is a machine-learning based RDR algorithm. The performance of proposed model was compared with that of 6 different machine-learning techniques. Our experimental results showed the proposing approach can help to deduct the cost of solving over-generalization and overfitting problems of machine learning approach.

Subject Categories:

  • Computer Systems Management and Standards
  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE