Intelligent phishing website detection system using fuzzy techniques.

Phishing websites are forged web pages that are created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying Phishing websites is really a complex and dynamic problem involving many factors and criteria, and because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Logic model can be an effective tool in assessing and identifying phishing websites than any other traditional tool since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the `fuzziness¿ in traditional website phishing risk assessment and propose an intelligent resilient and effective model for detecting phishing websites. The proposed model is based on FL operators which is used to characterize the website phishing factors and indicators as fuzzy variables and produces six measures and criteria¿s of website phishing attack dimensions with a layer structure. Our experimental results showed the significance and importance of the phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final phishing website rate

What is health information quality? Ethical dimension and perception by users

Introduction. The popularity of seeking health information online makes information quality (IQ) a public health issue. The present study aims at building a theoretical framework of health information quality (HIQ) that can be applied to websites and defines which IQ criteria are important for a website to be trustworthy and meet users’ expectations. Methods. We have identified a list of HIQ criteria from existing tools and assessment criteria and elaborated them into a questionnaire that was promoted via social media and mainly the University. Responses (329) were used to rank the different criteria for their importance in trusting a website and to identify patterns of criteria using hierarchical cluster analysis. Results. HIQ criteria were organized in five dimensions based on previous theoretical frameworks as well as on how they cluster together in the questionnaire response. We could identify a top-ranking dimension (scientific completeness) that describes what the user is expecting to know from the websites (in particular: description of symptoms, treatments, side effects). Cluster analysis also identified a number of criteria borrowed from existing tools for assessing HIQ that could be subsumed to a broad “ethical” dimension (such as conflict of interests, privacy, advertising policies) that were, in general, ranked of low importance by the participants. Subgroup analysis revealed significant differences in the importance assigned to the various criteria based on gender, nationality and whether or not of a biomedical educational background. Conclusions. We identified criteria of HIQ and organized them in dimensions. We observed that ethical criteria, while regarded highly in the academic and medical environment, are not considered highly by the public

Exploring the Most Visible German Websites on Melanoma Immunotherapy: A Web-Based Analysis

Background: Patients diagnosed with melanoma frequently search the internet for treatment information, including novel and complex immunotherapy. However, health literacy is limited among half of the German population, and no assessment of websites on melanoma treatment has been performed so far. Objective: The aim of this study was to identify and assess the most visible websites in German language on melanoma immunotherapy. Methods: In accordance with the common Web-based information-seeking behavior of patients with cancer, the first 20 hits on Google, Yahoo, and Bing were searched for combinations of German synonyms for “melanoma” and “immunotherapy” in July 2017. Websites that met our predefined eligibility criteria were considered for assessment. Three reviewers independently assessed their quality by using the established DISCERN tool and by checking the presence of quality certification. Usability and reliability were evaluated by the LIDA tool and understandability by the Patient Education Materials Assessment Tool (PEMAT). The Flesch Reading Ease Score (FRES) was calculated to estimate the readability. The ALEXA and SISTRIX tools were used to investigate the websites’ popularity and visibility. The interrater agreement was determined by calculating Cronbach alpha. Subgroup differences were identified by t test, U test, or one-way analysis of variance. Results: Of 480 hits, 45 single websites from 30 domains were assessed. Only 2 website domains displayed a German quality certification. The average assessment scores, mean (SD), were as follows: DISCERN, 48 (7.6); LIDA (usability), 40 (2.0); LIDA (reliability), 10 (1.6); PEMAT, 69% (16%); and FRES, 17 (14), indicating mediocre quality, good usability, and understandability but low reliability and an even very low readability of the included individual websites. SISTRIX scores ranged from 0 to 6872 and ALEXA scores ranged from 17 to 192,675, indicating heterogeneity of the visibility and popularity of German website domains providing information on melanoma immunotherapy. Conclusions: Optimization of the most accessible German websites on melanoma immunotherapy is desirable. Especially, simplification of the readability of information and further adaption to reliability criteria are required to support the education of patients with melanoma and laypersons, and to enhance transparency

ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment

Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.Comment: ICD