  In this paper, we uncover the essential features of websites that allow
intelligent models to distinguish between phishing and legitimate sites.
Phishing websites are those that are made with a similar user interface and a
near similar address to trustworthy websites in order to persuade users to
input their private data for potential future misuse by attackers. Detecting
phishing websites with intelligent systems is an important goal to protect
users, companies, and other online services that use the HTTP protocol. An
intelligent model needs to distinguish features that are important as input to
predict phishing sites. In this research, using correlation-based networks, we
provide a novel network-based method to find features that are more important
in phishing detection. The networks are trained and tested on an established
phishing dataset. Three different networks are made by partitioning the dataset
by its data instance labels. The important features are found by discovering
the hubs of these networks, and the results are presented and analysed. This is
the first time using a network-based approach for feature selection which is a
fast and accurate way to do so.
