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Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper.

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.

-Internet has become an essential component of our everyday social and financial activities. Nevertheless, internet-users may be vulnerable to different types of web-threats which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customer's confidence in ecommerce and online banking. Phishing is considered as a form of web-threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain confidential information such as usernames, passwords and social security number. So far, there is no single solution that can capture every phishing attack. In this article, we proposed an intelligent model for predicting phishing attacks based on Artificial Neural Network "ANN" particularly self-structuring neural networks. Phishing is a continuous problem where features significant in determining the type of webpages are constantly changing. Thus, we need to constantly improve the network structure in order to cope with these changes. Our model solves this problem by automating the process of structuring the network and shows high acceptance for noisy data, fault tolerance and high prediction accuracy. Several experiments were conducted in our research, the number of epochs differs in each experiment. From the results, we find that all produced structures have high generalization ability.