  With increasing technology developments, there is a massive number of
websites with varying purposes. But a particular type exists within this large
collection, the so-called phishing sites which aim to deceive their users. The
main challenge in detecting phishing websites is discovering the techniques
that have been used. Where phishers are continually improving their strategies
and creating web pages that can protect themselves against many forms of
detection methods. Therefore, it is very necessary to develop reliable, active
and contemporary methods of phishing detection to combat the adaptive
techniques used by phishers. In this paper, different phishing detection
approaches are reviewed by classifying them into three main groups. Then, the
proposed model is presented in two stages. In the first stage, different
machine learning algorithms are applied to validate the chosen dataset and
applying features selection methods on it. Thus, the best accuracy was achieved
by utilizing only 20 features out of 48 features combined with Random Forest is
98.11%. While in the second stage, the same dataset is applied to various fuzzy
logic algorithms. As well the experimental results from the application of
Fuzzy logic algorithms were incredible. Where in applying the FURIA algorithm
with only five features the accuracy rate was 99.98%. Finally, comparison and
discussion of the results between applying machine learning algorithms and
fuzzy logic algorithms is done. Where the performance of using fuzzy logic
algorithms exceeds the use of machine learning algorithms.
