Combining RDR-Based Machine Learning Approach and Human Expert Knowledge for Phishing Prediction
| Autor: | Renjie Chen, Soyeon Caren Han, Byeong Ho Kang, Hyunsuk Chung |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science Decision tree learning Decision tree 020207 software engineering 02 engineering and technology Information theory Machine learning computer.software_genre Phishing Data type Knowledge-based systems C4.5 algorithm Dynamic problem 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | PRICAI 2016: Trends in Artificial Intelligence ISBN: 9783319429106 PRICAI |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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