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.
Highlights
Phishing is a technique utilized by attackers to obtain user‟s sensitive information and financial account credential for financial benefit
This study focused in answering the question: how can parameter tuning method be used to maximize phishing detection accuracy using ANFIS with six sets of inputs? The aim is to design a parameter tuning method based on an adaptive neuro-fuzzy inference system, using comprehensive data from six inputs that can be used by researchers in the field
This study focuses on feature-based in phishing website detection, using adaptive neuro-fuzzy inference system
Summary
Phishing is a technique utilized by attackers to obtain user‟s sensitive information and financial account credential for financial benefit. According to the Press Association report, an increase in phishing attacks in online transaction caused losses of £21.6 million between January and June 2012, which was a growth of 28% from June 2011[1]. Due to this problem, various anti-phishing approaches have been proposed to solve the problem. Various anti-phishing approaches have been proposed to solve the problem These approaches include feature-based techniques [2], [3], blacklist-based [4], [5], [6], [7], and content-based approaches applying machine learning algorithms have attempted to solve the problem [8], [2]. Parameters are difficult to set to a desirable output, and parameter tuning framework are non-existent for phishing website detections [9]
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