Rami M. Mohammad

Predicting phishing websites based on self-structuring neural network

AbstractInternet 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 e-commerce 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 particularly self-structuring neural networks. Phishing is a continuous problem where features significant in determining the type of web pages 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, and the number of epochs differs in each experiment. From the results, we find that all produced structures have high generalization ability.

A dynamic self-structuring neural network model to combat phishing

Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing website classification problem and it shows competitive results with respect to various evaluation measures such as harmonic mean (F1-score), precision, and classification accuracy.

An intelligent model for trustworthiness evaluation in semantic web applications

The development and the advances of the World Wide Web from its first inception resulted in numerous diverse amounts of data and webpages on the web which motivates to greater demand for managing this data in an effective manner. Therefore, novel tools and techniques are required to effectively manage these data. Such tools are assumed to support interoperability and warehousing between the multiple data sources and extracting information from the different trusted databases on the web. Subsequently, the web is evolving into what is now called the Semantic Web aims to alleviate users from the burden of integrating different information sources as well as to perform searches. However, one needs to examine the trust issue of the searching results from any Semantic Web application. This paper investigates trust policies and mechanisms in semantic web applications and introduces an intermediary step to classify the relevant web services via intelligent model based on NN.