Automatic generation of meta classifiers with large levels for distributed computing and networking

This paper is devoted to a case study of a new construction of classifiers. These classifiers are called automatically generated multi-level meta classifiers, AGMLMC. The construction combines diverse meta classifiers in a new way to create a unified system. This original construction can be generated automatically producing classifiers with large levels. Different meta classifiers are incorporated as low-level integral parts of another meta classifier at the top level. It is intended for the distributed computing and networking. The AGMLMC classifiers are unified classifiers with many parts that can operate in parallel. This make it easy to adopt them in distributed applications. This paper introduces new construction of classifiers and undertakes an experimental study of their performance. We look at a case study of their effectiveness in the special case of the detection and filtering of phishing emails. This is a possible important application area for such large and distributed classification systems. Our experiments investigate the effectiveness of combining diverse meta classifiers into one AGMLMC classifier in the case study of detection and filtering of phishing emails. The results show that new classifiers with large levels achieved better performance compared to the base classifiers and simple meta classifiers classifiers. This demonstrates that the new technique can be applied to increase the performance if diverse meta classifiers are included in the system

An effective and secure mechanism for phishing attacks using a machine learning approach

Phishing is one of the biggest crimes in the world and involves the theft of the user's sensitive data. Usually, phishing websites target individuals' websites, organizations, sites for cloud storage, and government websites. Most users, while surfing the internet, are unaware of phishing attacks. Many existing phishing approaches have failed in providing a useful way to the issues facing e-mails attacks. Currently, hardware-based phishing approaches are used to face software attacks. Due to the rise in these kinds of problems, the proposed work focused on a three-stage phishing series attack for precisely detecting the problems in a content-based manner as a phishing attack mechanism. There were three input values-uniform resource locators and traffic and web content based on features of a phishing attack and non-attack of phishing website technique features. To implement the proposed phishing attack mechanism, a dataset is collected from recent phishing cases. It was found that real phishing cases give a higher accuracy on both zero-day phishing attacks and in phishing attack detection. Three different classifiers were used to determine classification accuracy in detecting phishing, resulting in a classification accuracy of 95.18%, 85.45%, and 78.89%, for NN, SVM, and RF, respectively. The results suggest that a machine learning approach is best for detecting phishing.Web of Science107art. no. 135

Establishing phishing provenance using orthographic features

After phishing message detection, determining the provenance of phishing messages and Websites is the second step to tracing cybercriminals. In this paper, we present a novel method to cluster phishing emails automatically using orthographic features. In particular, we develop an algorithm to cluster documents and remove redundant features at the same time. After collecting all the possible features based on observation, we adapt the modified global k-mean method repeatedly, and generate the objective function values over a range of tolerance values across different subsets of features. Finally, we identify the appropriate clusters based on studying the distribution of the objective function values. Experimental evaluation of a large number of computations demonstrates that our clustering and feature selection techniques are highly effective and achieve reliable results

Reducing the risk of e-mail phishing in the state of Qatar through an effective awareness framework

In recent years, cyber crime has focused intensely on people to bypass existing sophisticated security controls; phishing is one of the most common forms of such attack. This research highlights the problem of e-mail phishing. A lot of previous research demonstrated the danger of phishing and its considerable consequences. Since users behaviour is unpredictable, there is no reliable technological protective solution (e.g. spam filters, anti-viruses) to diminish the risk arising from inappropriate user decisions. Therefore, this research attempts to reduce the risk of e-mail phishing through awareness and education. It underlines the problem of e-mail phishing in the State of Qatar, one of world s fastest developing countries and seeks to provide a solution to enhance people s awareness of e-mail phishing by developing an effective awareness and educational framework. The framework consists of valuable recommendations for the Qatar government, citizens and organisations responsible for ensuring information security along with an educational agenda to train them how to identify and avoid phishing attempts. The educational agenda supports users in making better trust decisions to avoid phishing that could complement any technical solutions. It comprises a collection of training methods: conceptual, embedded, e-learning and learning programmes which include a television show and a learning session with a variety of teaching components such as a game, quizzes, posters, cartoons and a presentation. The components were tested by trial in two Qatari schools and evaluated by experts and a representative sample of Qatari citizens. Furthermore, the research proves the existence and extent of the e-mail phishing problem in Qatar in comparison with the UK where people were found to be less vulnerable and more aware. It was discovered that Qatar is an attractive place for phishers and that a lack of awareness and e-law made Qatar more vulnerable to the phishing. The research identifies the factors which make Qatari citizens susceptible to e-mail phishing attacks such as cultural, country-specific factors, interests and beliefs, religion effect and personal characteristics and this identified the need for enhancing Qatari s level of awareness on phishing threat. Since literature on phishing in Qatar is sparse, empirical and non-empirical studies involved a variety of surveys, interviews and experiments. The research successfully achieved its aim and objectives and is now being considered by the Qatari Government