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Combining RDR-based machine learning approach and human expert knowledge for phishing prediction


Chung, H and Chen, R and Han, SC and Kang, BH, Combining RDR-based machine learning approach and human expert knowledge for phishing prediction, Lecture Notes in Computer Science 9810: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016) - Trends in Artificial Intelligence, 22-26 August, Phuket, Thailand, pp. 80-92. ISSN 0302-9743 (2016) [Refereed Conference Paper]


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DOI: doi:10.1007/978-3-319-42911-3_7


Detecting 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.

Item Details

Item Type:Refereed Conference Paper
Keywords:phishing prediction, RDR, knowledge-based system, machine learning, decision tree
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Chung, H (Mr David Chung)
UTAS Author:Chen, R (Mr Renjie Chen)
UTAS Author:Han, SC (Ms Caren Han)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:113459
Year Published:2016
Deposited By:Engineering
Deposited On:2017-01-03
Last Modified:2018-02-01
Downloads:204 View Download Statistics

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