Phishing websites Dataset:

Phishing Websites is an ongoing problem. However, In order to overcome this problem several experiments should be conducted.

I have created a tool that extract phishing features from any website, so that you can build your up to date dataset and build your model.

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Phishing websites Dataset 

An Assessment of Features Related to Phishing Websites using an Automated Technique

An Assessment of Features Related to Phishing Websites using an Automated Technique

Abstract

Corporations that offer online trading can achieve a competitive edge by serving worldwide clients. Nevertheless, online trading faces many obstacles such as the unsecured money orders. Phishing is considered a form of internet crime that is defined as the art of mimicking a website of an honest enterprise aiming to acquire confidential information such as usernames, passwords and social security number. There are some characteristics that distinguish phishing websites from legitimate ones such as long URL, IP address in URL, adding prefix and suffix to domain and request URL, etc. In this paper, we explore important features that are automatically extracted from websites using a new tool instead of relying on an experienced human in the extraction process and then judge on the features importance in deciding website legitimacy. Our research aims to develop a group of features that have been shown to be sound and effective in predicting phishing websites and to extract those features according to new scientific precise rules.

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An Assessment of Features Related to Phishing Websites using an Automated Technique 

Intelligent Rule based Phishing Websites Classification

Intelligent Rule based Phishing Websites Classification

Abstract

Phishing is described as the art of emulating a website of a creditable firm intending to grab user’s private information such as usernames, passwords and social security number. Phishing websites comprise a variety of cues within its content-parts as well as browser-based security indicators. Several solutions have been proposed to tackle phishing. Nevertheless, there is no single magic bullet that can solve this threat radically. One of the promising techniques that can be used in predicting phishing attacks is based on data mining. Particularly the “induction of classification rules”, since anti-phishing solutions aim to predict the website type accurately and these exactly fit the classification data mining. In this paper, we shed light on the important features that distinguish phishing websites from legitimate ones and assess how rule-based classification data mining techniques are applicable in predicting phishing websites. We also experimentally show the ideal rule based classification technique for detecting phishing. 

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Intelligent Rule based Phishing Websites Classification

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Predicting Phishing Websites using Neural Network trained with Back-Propagation

Abstract

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates.

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Predicting Phishing Websites using Neural Network trained with Back-Propagation