The Classification of Phishing Websites using Supervised Data Mining Techniques

Phishing attacks are on the rise, and the consequences for businesses are severer. The impact of a phishing attack not only causes financial loss but also triggers data breaches. The data breaches caused by phishing attacks often lead to reputational damage and business disruption. Therefore, detecting potential phishing attempts has received tremendous attention. The purpose of this study is to identify the feature predicting the presence of a phishing site by using the public phishing URL dataset. The dataset used in this study includes 87 predictor variables across three distinct feature groups, including 1) 56 URL-based features obtained by analyzing the text of URLs, 2) 24 Content-based features extracted by loading the web pages of URLs and analyzing their HTML contents, 3) and seven external features obtained by querying reference third party services and search engines. The top-7 most meaningful inputs from each feature group are selected and analyzed in three different supervised data mining techniques to determine which feature group produces the most robust model for classifying and detecting phishing websites. The result of this study shows that the inputs from the external features group consistently had the highest Accuracy, Specificity, Sensitivity, and Precision across all supervised data mining techniques. This study also finds that the model can be improved by using a combination of inputs from all three feature groups, including 3 URL-based features, 2 Content-based features, and 2 External features. The result of this study will help shape and strengthen security awareness training for organizations and be used as the foundation for building preventative tools for both individuals and companies against phishing attacks

Report on the EHCR (Deliverable 26.2)

This deliverable is the second for Workpackage 26. The first, submitted after Month 12, summarised the areas of research that the partners had identified as being relevant to the semantic indexing of the EHR. This second one reports progress on the key threads of work identified by the partners during the project to contribute towards semantically interoperable and processable EHRs. This report provides a set of short summaries on key topics that have emerged as important, and to which the partners are able to make strong contributions. Some of these are also being extended via two new EU Framework 6 proposals that include WP26 partners: this is also a measure of the success of this Network of Excellence

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

A comparative study of the AHP and TOPSIS methods for implementing load shedding scheme in a pulp mill system

The advancement of technology had encouraged mankind to design and create useful equipment and devices. These equipment enable users to fully utilize them in various applications. Pulp mill is one of the heavy industries that consumes large amount of electricity in its production. Due to this, any malfunction of the equipment might cause mass losses to the company. In particular, the breakdown of the generator would cause other generators to be overloaded. In the meantime, the subsequence loads will be shed until the generators are sufficient to provide the power to other loads. Once the fault had been fixed, the load shedding scheme can be deactivated. Thus, load shedding scheme is the best way in handling such condition. Selected load will be shed under this scheme in order to protect the generators from being damaged. Multi Criteria Decision Making (MCDM) can be applied in determination of the load shedding scheme in the electric power system. In this thesis two methods which are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were introduced and applied. From this thesis, a series of analyses are conducted and the results are determined. Among these two methods which are AHP and TOPSIS, the results shown that TOPSIS is the best Multi criteria Decision Making (MCDM) for load shedding scheme in the pulp mill system. TOPSIS is the most effective solution because of the highest percentage effectiveness of load shedding between these two methods. The results of the AHP and TOPSIS analysis to the pulp mill system are very promising

Semantic business process management: a vision towards using semantic web services for business process management

Business process management (BPM) is the approach to manage the execution of IT-supported business operations from a business expert's view rather than from a technical perspective. However, the degree of mechanization in BPM is still very limited, creating inertia in the necessary evolution and dynamics of business processes, and BPM does not provide a truly unified view on the process space of an organization. We trace back the problem of mechanization of BPM to an ontological one, i.e. the lack of machine-accessible semantics, and argue that the modeling constructs of semantic Web services frameworks, especially WSMO, are a natural fit to creating such a representation. As a consequence, we propose to combine SWS and BPM and create one consolidated technology, which we call semantic business process management (SBPM