Original Websites Research Articles

Detection of Phishing Sites Using Machine Learning Techniques

Phishing is a very commonly occurring attack in which the attacker attempt to get the private information of the user like card details, their passwords their transaction details etc. using fake copy websites. Attackers uses the websites very similar to the original websites and not possible for common people to identify. Phishing is the biggest loop-hole in the cyber world. Phishing became a successful business for phishers. Other than fake websites phishers uses different methods to do this job using messaging, spoofed links to make money and to counter Phishing various method are proposed some anti-phishing techniques are blacklist, whitelist but due to exponential growth on new innovations and technologies these techniques falls a little below as new websites contains dynamically work in it due to that these techniques cannot outperform todays websites.

A Feature Extraction Approach for the Detection of Phishing Websites Using Machine Learning

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users’ sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user’s accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity.

ОБУЧЕНИЕ АНГЛИЙСКОМУ ЯЗЫКУ СТУДЕНТОВ ТВОРЧЕСКИХ СПЕЦИАЛИЗАЦИЙ В ВУЗЕ КУЛЬТУРЫ: ТРАДИЦИИ И НОВАЦИИ

Сhanging demands for the results of acquiring universal competencies dictate the necessity of using new educational technologies alongside with the tradi-tional methods in teaching English as a foreign language to the students of creative specialties at the higher educational institution of culture. The author proves the effectiveness of using the digital content of the English schools and original web-sites in the educational process which contribute to forming nec-essary communicative skills. The article illustrates the effectiveness of such a mixed format of activity that combines digital technologies and traditional forms of working over the class materials to stimulate the learners’ cognitive activity, develop their speech and thinking capacities, creative abilities.

A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN

In terms of the Internet and communication, security is the fundamental challenging aspect. There are numerous ways to harm the security of internet users; the most common is phishing, which is a type of attack that aims to steal or misuse a user’s personal information, including account information, identity, passwords, and credit card details. Phishers gather information about the users through mimicking original websites that are indistinguishable to the eye. Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft. Therefore, there is a strong need to develop a system that efficiently detects phishing websites. Three distinct deep learning-based techniques are proposed in this paper to identify phishing websites, including long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and lastly an LSTM–CNN-based approach. Experimental findings demonstrate the accuracy of the suggested techniques, i.e., 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The proposed phishing detection method demonstrated by the CNN-based system is superior.

A Critical Appraisal of Web-Based Information on Shoulder Pain Comparing Biomedical Versus Psychosocial Information.

Websites have become a primary way for patients to access health-related information which allows patients to not only understand their condition better but also to engage in better decision-making with their healthcare provider. However, this can be a double-edged sword, as information patients access may not be of high quality, easily readable, or could be biased based on website authorship. This study examines the readability and content quality of common websites about shoulder pain, with a specific focus on biomedical versus psychosocial information. The Flesch reading-ease score (FRES) and Flesch-Kincaid grade level (FKGL) instruments were used to assess website readability. Health on the net code of conduct (HONcode) certification and the DISCERN tool were used to evaluate the reliability and quality of information. Lastly, shoulder-specific content quality and focus was gauged using a shoulder-specific website appraisal tool (SWAT) created for this study. A systematic search protocol was used to identify popular shoulder injury websites. The websites were identified using 5 search terms (shoulder pain, shoulder muscle pain, shoulder impingement, shoulder rotator cuff pain, and shoulder pain diagnosis) across 5 English-speaking regions (US, Canada, UK, Australia, and New Zealand) using the Google search engine. The top 10 websites for each term and region combination were included and combined, yielding 41 original websites for appraisal; several (6) websites were omitted as duplicates, behind paywalls, or non-text (video) for a total of 35 websites appraised. On average, the FRES shoulder pain websites readability was 55.37, which is categorized as "fairly difficult" to read, and a 7th or 8th grade reading level based on the FKGL. For trustworthiness and bias, 57% (20 out of 35) of the websites were HONcode certified. The quality of healthcare information utilizing the DISCERN score averaged 50.92%. Examining shoulder-specific content quality, the average SWAT score was 10.54 out of 14 possible points, with only one website reporting information on psychosocial aspects of shoulder pain. This study suggests that shoulder pain websites present information that is at too high of a reading level for public consumption, has a strong bias towards biomedical causes of shoulder pain, and would benefit from implementation of website screening tools to improve evidence-based content.

Detection of Phishing Websites Using Deep Learning Techniques

Phishing is a deceitful trick of cyber-attack designed and implemented by scammers and hackers with purpose of stealing personal data by impersonating the original websites. Phishing is like fishing in a lake wherein the users are very conveniently be fooled by scammers (phishers) by impersonating original websites and contents to leak their valuable personal and professional data. Currently a lot of anti phishing tools and techniques are being applied to detect and nullify the phishing cyber threat viz, heuristic feature, blacklist or white list and visual similarity-based approaches. In this research paper, we have anticipated robust and novel anti-phishing models via (I) Long Short-Term Memory (LSTM), (II) Deep-Neural Network (DNN) and (III) Convolution-Neural Network (CNN) using 10 features. The anticipated model achieves an accuracy of 98.67% for LSTM, 96.33% for DNN and 97.23% for CNN. The proposed techniques are highly efficient and robust which increases the phishing detection manifold.

A Survey on Phishing Website Detection Using Hadoop

Phishing is an activity carried out by phishers with the aim of stealing personal data of internet users such as user IDs, password, and banking account, that data will be used for their personal interests. Average internet user will be easily trapped by phishers due to the similarity of the websites they visit to the original websites. Because there are several attributes that must be considered, most of internet user finds it difficult to distinguish between an authentic website or not. There are many ways to detecting a phishing website, but the existing phishing website detection system is too time-consuming and very dependent on the database it has. In this research, the focus of Hadoop MapReduce is to quickly retrieve some of the attributes of a phishing website that has an important role in identifying a phishing website, and then informing to users whether the website is a phishing website or not.

Web archiving: Policy and practice

The UC San Diego Library has been collecting and providing access to archived web content since 2007. Initial collections were created on an ad hoc basis, with no highlevel plan to identify websites and content of interest, and there was little documentation of how early collection decisions were made. As time passed, the library’s web archiving efforts increased in scale, and outgrew this informal approach. Efforts were made to standardise web archiving processes and policies via collection request forms and standardised metadata, eventually culminating in the creation of a web archive collection development policy, and collection and quality control workflows and tracking. This article outlines the process of creating these tools, including establishing institutional needs and concerns, evaluating the wider landscape of web archiving policies and norms, and considering sustainable use of available resources. The article also discusses future areas of work to ensure that web content of research and historical interest is captured in full, preserved responsibly, and made accessible even when the original websites have changed or disappeared.

Phishing web site detection using diverse machine learning algorithms

PurposeThis paper aims to present a framework to detect phishing websites using stacking model. Phishing is a type of fraud to access users’ credentials. The attackers access users’ personal and sensitive information for monetary purposes. Phishing affects diverse fields, such as e-commerce, online business, banking and digital marketing, and is ordinarily carried out by sending spam emails and developing identical websites resembling the original websites. As people surf the targeted website, the phishers hijack their personal information.Design/methodology/approachFeatures of phishing data set are analysed by using feature selection techniques including information gain, gain ratio, Relief-F and recursive feature elimination (RFE) for feature selection. Two features are proposed combining the strongest and weakest attributes. Principal component analysis with diverse machine learning algorithms including (random forest [RF], neural network [NN], bagging, support vector machine, Naïve Bayes and k-nearest neighbour) is applied on proposed and remaining features. Afterwards, two stacking models: Stacking1 (RF + NN + Bagging) and Stacking2 (kNN + RF + Bagging) are applied by combining highest scoring classifiers to improve the classification accuracy.FindingsThe proposed features played an important role in improving the accuracy of all the classifiers. The results show that RFE plays an important role to remove the least important feature from the data set. Furthermore, Stacking1 (RF + NN + Bagging) outperformed all other classifiers in terms of classification accuracy to detect phishing website with 97.4% accuracy.Originality/valueThis research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.

AN EFFICIENT ALGORITHM TO IDENTIFY PHISHING SITES USING URL DOMAIN FEATURES

People are using internet for all their day to day activities like shopping, banking, mailing etc. The term phishing is a kind of spoofing website which is used to steal important information. There is a fortune to steal all our personal data by doing something as fraudulent. So there is a need of efficient prevention mechanism to find out the phishing webpage among the legitimate web pages. This paper proposes a study on identifying phishing websites by developing an effective algorithm with domain features of URL. This algorithm is used to overcome the difficulty and complexity of detecting phishing websites which looks exactly like an original websites.

A Review on the Major Global University Ranking Systems and the Turkish Universities’ Overall Position in Rankings

Amidst the increasingly competitive environment in the field of higher education, world ranking systems have gained more research attention in recent years. In this article, the main objectives are to attract attention of especially Turkish academics and policy makers to university ranking systems in the world and to show the overall position of Turkish universities in these ranking systems. With this aim, the major rankings were specially examined in terms of their measurements from the original websites of them. As a result of this review, the position of Turkish universities among the top 500 universities in these rankings over a six-year period are determined from 2011 to 2016; then, obtained data through the original websites of these ranking systems and the reports of the URAP are compiled and presented in tables. Two issues can be observed from the study: research performance is one of the major indicators used in all world ranking systems, and the overall position of Turkish universities in such rankings is not very good. This article can help all staff in academia provide the better understand the goals, importance and measurements of ranking systems and Turkish Universities’ position in these rankings. Thus, the study tries to create awareness about the need of improving their position. After this paper, the further research is likely to examine the concept of research performance as an important criterion of determining the universities’ position in the major world rankings to obtain a broad and comprehensive perspective.

Website Translation and Destination Image Marketing

This study compares the image representations of Reunion Island on the Internet when using original websites in French and their English version. It analyzes the text content of web pages obtained from a list of web information sources providing both French and English versions. Textual data analysis and correspondence analysis based on the most frequent keywords is conducted using the software package Alceste. Results show that the image of Reunion Island is very different between language versions. These differences can be explained by the poor quality of the language translation even though the websites studied were retrieved from the top list of tourism websites providing information on Reunion Island as a destination. This study has strategic implications, as it demonstrates a failure to implement effective and consistent destination marketing by tourism organizations that will confuse the consumer. We make recommendations for future research and practice in terms of improved strategic, tactical, and operational integration.

Microbial bioremediation products

An annotated selection of world wide websites relevant to the topics in environmental microbiology This is a peer-reviewed journal article discussing the efficacy of ten bioremediation products in the cleanup of oil that had been spilled along the Alaska coast. This paper describes the effectiveness of bioremediation for treating oil spilled in Alaska by the Exxon–Valdez tanker. This site provides a large list of bioremediation products and services with links to the original websites describing the materials. This site put up by the American Society for Microbiology provides a good summary of the principles and practices of bioremediation. This site is dated but it contains specific examples of sites treated by bioremediation with links to further information. This list is provided by the Biocatalysis/Biodegradation Database and has links for each organism to its known biodegradation pathways. CandidatusAccumulibacterphosphatis removes large amounts of phosphate from waters and stores it as polyphosphate; as such this microbe is useful in phosphate bioremediation and sequestration. This webpage contains the genome sequence data for a phosphate-removing microbe used in phosphate remediation and sequestration. This site provides information on waste sites at which bioremediation is being used or considered to be used. This database defines some general terms and offers a brief list of bioremediation services provided within Hawaii. This site contains a description of a bioremediation technology for treating waters derived from unconventional oil and gas extraction, also known as hydraulic fracturing. This site contains an article that makes the case for using bioremediation as a tool for dealing with waters derived from hydraulic fracturing operations. This is an example of a technical product bulletin for a bioremediation product posted by the U.S. Environmental Protection Agency. Regenesis' products for bioremediation generally revolve around the concept of reagent biostimulation to enhance the biodegradative activity of intrinsic microorganisms. The product that is used by JRW Bioremediation is often determined by the conditions at the site and the decision of what substrate to add to support the appropriate bioremediation process. This page provides a general treatment of bioremediation and is found on the Microbe Wiki site. This web book put out by the U.S. National Academy of Sciences is a general treatise on bioremediation. This website provides describes microbial communities and methods of study that are involved in bioremediation to treat wastewater streams. This site describes the use of a microbial product to treat waste hydrocarbons from the oil and gas industries. CBI is a large company that does many types of remediation and environmental restoration, including bioremediation. This subsidiary of Haliburton deals with the microbial remediation of hydrocarbons derived from oil and gas drilling. This article describes bioremediation, largely in the context of the Deepwater Horizon spill and the ability of microbes to handle oil and derivative wastes.

A corpus-based error typology: towards a more objective approach to measuring quality in localization

This paper examines current localization quality assessment through a product-based analysis of error typologies and the role they play in producing reliable and valid measurements. In order to provide a more objective foundation for localization quality, the methodology used is based on a monolingual comparable corpus of original and localized Spanish corporate websites. The contrastive analysis of this corpus highlighted recurrent inadequacies that are difficult to control with current evaluation metrics and, as a result, a comprehensive error categorization for localization is presented. It incorporates certain aspects, such as pragmatic errors, that are absent in current testing instruments used in the industry. In order to observe whether this typology can provide a more holistic and valid approach, it is applied to a case study that measures the errors in a localized Spanish website from one of the largest US companies. The results shed some light onto the shortcomings of current Quality Analysis practices and illustrate the benefits of adopting a bottom-up corpus approach in quality assessment. The wider implications for testing practices in localization are also described.

Research on the Status and Operating Model of the Original Net Literary Websites in China

With the development and progress of Internet technology, the network has increasingly become a key part of daily life and challenged some traditional customs, and reading is one of the very significant areas. And the birth of the net literature is almost obvious, and some literary phenomena related to this are not a coincidence. At the same time, the wave of commercialization in the network has been leading the operating model of original literary websites to be mature, which has a great impact on the traditional publishing. Original literary website provides a platform for the development of net literature, along with this, the authors and readers of net literature has generally expanded and there are the new trends of resource integration and industrialization. However, a series of problem still exists, including copyright protection and content quality control. For the traditional publishing industry, original net literary websites are both partners and competitors, because they have different resources, meanwhile, the combination of them is no longer new. The operating mode of original net literary websites can also provide experience to the traditional publishing industry about how to develop digital publishing. This article will focus on the status and operating model of original literary websites, and make a comprehensive analysis.

Remediating jihad for western news audiences: The renewal of gatekeeping?

Digitization creates an ontological challenge to broadcast-era metaphors (gate, channel, flow), not least to understandings of who news gatekeepers are, where gates lie, the presumed audience, community or culture gatekeeping is done for, and what it means to gatekeep. Analysing how jihadist speeches by bin Laden, Al-Zawahiri and others are translated and remediated from their original websites, languages and contexts by various intermediaries and by western mainstream news, including the BBC, illuminates an apparently simple, settled gatekeeping model that produces systematic patterns of translation, selection and omission. Western news creates an obstacle to understanding why such texts may be appealing to some audiences by ignoring intermediaries such as terrorism-monitoring sites, Arabic media, and jihadist websites’ own self-monitoring services, delimiting a ‘mainstream’ understanding. A focus on multilingual, multiplatform gatekeeping demonstrates how loci and forms of power and authority are changing in the ‘connective turn’, to which media practitioners and scholars must adapt.

“Sentences like these:” Multicultural information dynamics and international diversity of thought

Multicultural information dynamics is exploratory cross-cultural research of the information-seeking behavior of a group of eighty-four Egyptian and American reference librarians asked to choose from websites in different languages. This paper, the fourth in a series, focuses on national, monolingual, and multilingual subgroups, and provides multi-tiered analyzes of websites clicked, reasons given for clicking, preferences for machine translations vs. original foreign language websites, decision making when choosing non-native language hits, and foreign language anxiety. Findings of the research show that information seekers of both nationalities are reluctant to cross cultural lines at the basic level of retrieved Internet information hits. Further results delineate differences and similarities in motivations, circumstantial preferences for original languages or machine translations, and comparative information-seeking behavior of subgroups. The research has implications for improving search performance in the fields of global knowledge dissemination via website and search engine design, library science, and international scholarship.

“Sentences like these:” Multicultural information dynamics and international diversity of thought

Multicultural information dynamics is exploratory cross-cultural research of the information-seeking behavior of a group of eighty-four Egyptian and American reference librarians asked to choose from websites in different languages. This paper, the fourth in a series, focuses on national, monolingual, and multilingual subgroups, and provides multi-tiered analyzes of websites clicked, reasons given for clicking, preferences for machine translations vs. original foreign language websites, decision making when choosing non-native language hits, and foreign language anxiety. Findings of the research show that information seekers of both nationalities are reluctant to cross cultural lines at the basic level of retrieved Internet information hits. Further results delineate differences and similarities in motivations, circumstantial preferences for original languages or machine translations, and comparative information-seeking behavior of subgroups. The research has implications for improving search performance in the fields of global knowledge dissemination via website and search engine design, library science, and international scholarship.

Improving Website Usability with Latent Semantic Analysis

When people search for information on the Web, they rely on link descriptions to decide where to look. If the descriptions are not sufficiently accurate, people will have a hard time navigating. This difficulty is what is known as the vocabulary problem. The overall goal of this study was to develop a way of making the vocabulary problem less probable on websites: first by improving the information scent of links in websites, and second by investigating the relationship between information scent and website navigability. Information scent was defined as the semantic similarity between a link description and its target information, quantified using Latent Semantic Analysis (LSA). Lists of keywords related to web pages were obtained from human participants and from LSA, and then the keywords were used to make modifications to the links on web pages. Participants showed faster navigation when using the modified websites compared to using the original websites.