Intrusion Detection Systems with Live Knowledge System

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.

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Document Details

Document Type
Technical Report
Publication Date
May 31, 2016
Accession Number
AD1025911

Entities

People

  • Byeong H. Kang

Organizations

  • University of Tasmania

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Languages
  • Detection
  • Electronic Mail
  • Expert Systems
  • Information Systems
  • Intrusion
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Machine Learning
  • Neural Networks
  • Supervised Machine Learning
  • Training

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Artificial Intelligence
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks