A new fast associative classification algorithm for detecting phishing websites
| Autor: | Samer Alhawari, Wael Hadi, Faisal Aburub |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Measure (data warehouse)
Association rule learning Computer science business.industry Supervised learning 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Phishing Statistical classification ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Algorithm Software Associative property |
| Zdroj: | Applied Soft Computing. 48:729-734 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2016.08.005 |
| Popis: | Display Omitted A new fast Associative classification mining approach is developed.The applicability of well-known associative classification techniques on detecting phishing websites is investigated.Experimental results using different associative classification algorithms was performed. Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast Associative Classification Algorithm (FACA). We investigate our proposed algorithm against four well-known AC algorithms (CBA, CMAR, MCAR, and ECAR) on real-world phishing datasets. The bases of the investigation in our experiments are classification accuracy and the F1 evaluation measures. The results indicate that FACA is very successful with regard to the F1 evaluation measure compared with the other four well-known algorithms (CBA, CMAR, MCAR, and ECAR). The FACA also outperformed the other four AC algorithms with regard to the accuracy evaluation measure. |
| Databáze: | OpenAIRE |
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