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

Using network structure and community detection to discover important website features when distinguishing between phishing and legitimate ones