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

Citation

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


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Official URL: http://pkaw.org/pkaw2016/

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

Abstract

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 and Image Processing
Research Field:Expert Systems
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Chung, H (Mr David Chung)
Author:Chen, R (Mr Renjie Chen)
Author:Han, SC (Ms Caren Han)
Author:Kang, BH (Professor Byeong Kang)
ID Code:113459
Year Published:2016
Deposited By:Engineering
Deposited On:2017-01-03
Last Modified:2017-07-10
Downloads:9 View Download Statistics

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