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Combining RDR-based machine learning approach and human expert knowledge for phishing prediction
conference contribution
posted on 2023-05-23, 11:41 authored by Hyunsuk Chung, Chen, R, Han, SC, Byeong KangByeong KangDetecting phishing websites has been noted as complex and dynamic problem area because of the subjective considerations and ambiguities of detection mechanism. We propose a novel approach that uses Ripple-down Rule (RDR) to acquire knowledge from human experts with the modified RDR model-generating algorithm (Induct RDR), which applies machine-learning approach. The modified algorithm considers two different data types (numeric and nominal) and also applies information theory from decision tree learning algorithms. Our experimental results showed the proposing approach can help to deduct the cost of solving over-generalization and over-fitting problems of machine learning approach. Three models were included in comparison: RDR with machine learning and human knowledge, RDR machine learning only and J48 machine learning only. The result shows the improvements in prediction accuracy of the knowledge acquired by machine learning.
History
Publication title
Lecture Notes in Computer Science 9810: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016) - Trends in Artificial IntelligenceVolume
9810Editors
R Booth & M-L ZhangPagination
80-92ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
Springer International PublishingPlace of publication
SwitzerlandEvent title
14th Pacific Rim International Conference on Artificial IntelligenceEvent Venue
Phuket, ThailandDate of Event (Start Date)
2016-08-22Date of Event (End Date)
2016-08-26Rights statement
Copyright unknownRepository Status
- Open