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Digital Object Identifier (DOI) : 10.14569/IJARAI.2014.031003
Article Published in International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 3 Issue 10, 2014.
Abstract: Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field.
P. A. Barraclough, G. Sexton, M.A. Hossain and N. Aslam, “Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 3(10), 2014. http://dx.doi.org/10.14569/IJARAI.2014.031003