Similar Web Pages Research Articles

APIMiner: Identifying Web Application APIs Based on Web Page States Similarity Analysis

Modern web applications offer various APIs for data interaction. However, as the number of these APIs increases, so does the potential for security threats. Essentially, more APIs in an application can lead to more detectable vulnerabilities. Thus, it is crucial to identify APIs as comprehensively as possible in web applications. However, this task faces challenges due to the increasing complexity of web development techniques and the abundance of similar web pages. In this paper, we propose APIMiner, a framework for identifying APIs in web applications by dynamically traversing web pages based on web page state similarity analysis. APIMiner first builds a web page model based on the HTML elements of the current web page. APIMiner then uses this model to represent the state of the page. Then, APIMiner evaluates each element’s similarity in the page model and determines the page state similarity based on these similarity values. From the different states of the page, APIMiner extracts the data interaction APIs on the page. We conduct extensive experiments to evaluate APIMiner’s effectiveness. In the similarity analysis, our method surpasses state-of-the-art methods like NDD and mNDD in accurately distinguishing similar pages. We compare APIMiner with state-of-the-art tools (e.g., Enemy of the State, Crawlergo, and Wapiti3) for API identification. APIMiner excels in the number of identified APIs (average 1136) and code coverage (average 28,470). Relative to these tools, on average, APIMiner identifies 7.96 times more APIs and increases code coverage by 142.72%.

A Hybrid Mechanism for Auto Text Categorization in Web Documents

Web personalization has become such a popular paradigm nowadays, that almost all e-commerce websites are including it in their websites. The main objective of web personalization is driven by grouping similar web pages. The text categorization principle becomes a challenge when daily users visit numerous pages. This paper develops a hybrid framework which categorizes the text extracted from a web document, by applying Neighbourhood Preserving Embedding algorithm and then Particle Swarm Optimization algorithm on the extracted text groups, resulting into a group of web documents which contain similar texts. The proposed mechanism relatively has a high performance which improves with time, and as the size of web documents increase, the particle swarm algorithm also evolves in its nature.

Pregnancy and birth cohorts in Europe

A birth cohort study is a form of study that uses expectant mothers and newborn as research subjects. This form of research is suitable for epidemiological studies, which pregnancy and birth cohort studies generally are. Data is collected in order to identify health consequences and overall health outcomes. The main aim of this paper is to review and summarize all cohort studies that have been carried out or are still being conducted in Europe, as well as general information such as aim of the study, number of participants and duration of follow-up. Gathering information was made easier by websites such as Birthcohorts, CHICOS and LifeCycle, where many of the cohorts and their sources are listed. The remaining data was found by searching Google Scholar, PubMed and similar webpages, using keywords ‘birth and pregnancy cohorts’, ‘infants’, ‘pregnancies’, ‘allergies’ and ‘childhood obesity’. Overall, 137 cohorts in 27 countries were found. They are a great and important source of information and for the most part, concentrate on general health and wellbeing of mothers and children. Collaboration between different birth cohorts is crucial for further harmonization of collected data and their use in the public health systems worldwide

Semantic Table-of-Contents for Efficient Web Screen Reading.

Navigating back-and-forth between segments in webpages is well-known to be an arduous endeavor for blind screen-reader users, due to the serial nature of content navigation coupled with the inconsistent usage of accessibility enhancing features such as WAI-ARIA landmarks and skip navigation links by web developers. Without these supporting features, navigating modern webpages that typically contain thousands of HTML elements in their DOMs, is both tedious and cumbersome for blind screen-reader users. Existing approaches to improve non-visual navigation efficiency typically propose 'one-size-fits-all' solutions that do not accommodate the personal needs and preferences of screen-reader users. To fill this void, in this paper, we present sTag, a browser extension embodying a semi-automatic method that enables users to easily create their own Table Of Contents (TOC) for any webpage by simply 'tagging' their preferred 'semantically-meaningful' segments (e.g., search results, filter options, forms, menus, etc.) while navigating the webpage. This way, all subsequent accesses to these segments can be made via the generated TOC that is made instantly accessible via a special shortcut or a repurposed mouse/touchpad action. As tags in sTag are attached to the abstract semantic segments instead of actual DOM nodes in the webpage, sTag can automatically generate equivalent TOCs for other similar webpages, without requiring the users to duplicate their tagging efforts from scratch in these webpages. An evaluation with 15 blind screen-reader users revealed that sTag significantly reduced the content-navigation time and effort compared to those with a state-of-the-art solution.

SpoofCatch: A Client-Side Protection Tool Against Phishing Attacks

To protect against web spoofing attacks, most antiphishing solutions in the literature either escape certain attack patterns or are based on complex sets of parameters to identify phishing attacks or suffer from both. In this article, we propose that phishing attacks can be prevented by simply relying on an overall visual appearance of the web page that the user sees. To realize our claim, we propose a client-side protection mechanism based on visual similarity of web pages and implement our mechanism as a browser extension, dubbed SpoofCatch. For similarity comparison between genuine and phished web pages, four similarity algorithms have been implemented and integrated in the extension. To evaluate the solution, large scale and extensive experiments have been conducted that demonstrate SpoofCatch can capture all phishing attacks with acceptable overhead.

Modeling an web community discovery method with web page attraction

An improved Web community discovery algorithm is proposed in this paper based on the attraction between Web pages to effectively reduce the complexity of Web community discovery. The proposed algorithm treats each Web page in the Web pages collection as an individual with attraction based on the theory of universal gravitation, elaborates the discovery and evolution process of Web community from a Web page in the Web pages collection, defines the priority rules of Web community size and Web page similarity, and gives the calculation formula of the change in Web page similarity. Finally, an experimental platform is built to analyze the specific discovery process of the Web community in detail, and the changes in cumulative distribution of Web page similarity are discussed. The results show that the change in the similarity of a new page satisfies the power-law distribution, and the similarity of a new page is proportional to the size of Web community that the new page chooses to join.

Deep Neural Network with Stacked Denoise Auto Encoder for Phishing Detection

Sensitive information such as credit card information, username, password and social security number etc, can be stolen using a fake page that imitates trusted website is called phishing. The attacker designs a similar webpage either by copying or making small manipulation to the legitimate page so that the online user cannot distinguish the legitimate and fake websites. A Deep Neural Network (DNN) was introduced to detect the phishing Uniform Resource Locator (URL). Initially, a 30-dimension feature vector was constructed based on URL-based features, Hypertext Markup Language (HTML)-based features and domain-based features. These features were processed in DNN to detect the phishing URL. However, the irrelevant, redundant and noisy features in the dataset increase the complexity of DNN classifier. So the feature selection is required for efficient phishing attack detection. But feature selection is a time-consuming process since it is an independent process. So in this paper, a feature vector is generated by DNN itself using Stacked Denoise Auto Encoder (SDAE). Moreover, the noisy data such as missing features affect the efficiency of phishing detection so the SDAE is trained to reconstruct a clean input feature vector. The initial input feature vector is corrupted by setting some feature vectors as zero. Then the corrupted feature vector is then mapped with basic auto encoder, to a hidden representation from which the input feature vector is reconstructed. The reconstructed features are given as input to DNN which selects the most relevant features and predicts the phishing URL. Hence the sparse feature representation of SDAE increases the classification accuracy of DNN. The experiments are conducted in Ham, Phishing Corpus and Phishload datasets in terms of accuracy, precision, recall and F-measure to prove the effectiveness of DNN-SDAE.

Algorithm of web page similarity comparison based on visual block

Phishing often deceives users due to the relative similarity to the true pages on a layout and leads to considerable losses for the society. Consequently, detecting phishing sites has been an urgent activity. By researching phishing web pages using web page screenshots, we discover that this kind of web pages use numerous web page screenshots to achieve the close similarity to the true page and avoid the text and structure similarity detection. This study introduces a new similarity matching algorithm based on visual blocks. First, the RenderLayer tree of the web page is obtained to extract the visual block. Second, an algorithm that will settle the jumbled visual blocks, including the deletion of the small visual blocks and the emergence of the overlapping visual blocks, is designed. Finally, the similarity between the two web pages is assessed. The proposed algorithm sets different thresholds to achieve the optimal missing and false alarm rates.

A hybridized semantic trust-based framework for personalized web page recommendation

ABSTRACT The World Wide Web is constantly evolving and is the most dynamic information repository in the world that has ever existed. Since the information on the web is changing continuously and owing to the presence of a large number of similar web pages, it is very challenging to retrieve the most relevant information. With a large number of malicious and fake web pages, it is required to retrieve Web Pages that are trustworthy. Personalization of the recommendation of web pages is certainly necessary to estimate the user interests for suggesting web pages as per their choices. Moreover, the Web is tending towards a more organized Semantic Web which primarily requires semantic techniques for recommending the Web Pages. In this paper, a framework for personalized web page recommendation based on a hybridized strategy is proposed. Web Pages are recommended based on the user query by analyzing the Web Usage Data of the users. An array of strategies is intelligently integrated together to achieve an efficient Web Page Recommendation system. Latent Semantic Analysis is applied to the User-Term Matrix and the Term-Frequency Matrix that are built from the Web Usage Information to form a Term Prioritization Vector. Further, techniques like Latent Dirichlet Allocation for Topic-based Segregation of the URLs and Normalized Pointwise Mutual Information strategies are used for recommending web pages based on users’ queries. The Personalization is achieved by prioritizing the Web pages based on the Prioritization Vector. Also, a unique methodology is incorporated into the system to retrieve trustworthy websites. An overall Accuracy of 0.84 is achieved which is better than the existing strategies.

Layout-based computation of web page similarity ranks

In this paper, we propose a ranking approach which considers visual similarities among web pages by using structure and vision-based features. Throughout the study, we aim to understand and represent the web page visual structure as in the way people do by focusing on the layout similarity through the wireframe design. The conducted study is composed of two parts. In the first part, structural similarities are analyzed with the proposed concept of “layout components” along with visual inspection of DOM trees. In this way, five types of structural layout components are proposed and revealed. Moreover, whitespaces are also utilized since they are important visual cues in the visual perception of web pages. In the second part, a computer-vision based method named histogram of oriented gradients (HOG) is employed to reveal local visual cues in terms of edge orientations. Following the feature extraction phases, extracted feature histograms are mapped on spatial information preserving multilevel and multi-resolution bag of features representation method named spatial pyramid matching. In this way, three goals were achieved: (1) the visual layout of web pages were mapped and compared in a multi-resolution schema; (2) the intermediate process of visual segmentation was removed; and (3) efficient and easily comparable web page layout signatures were generated. We also conducted a questionnaire study covering 312 subjects. This helped us to create a benchmark dataset involving similarity scores collected from individuals. So far, there exists no web page layout similarity ranking oriented corpus in the literature. Our suggested approach achieved a remarkable ranking performance at top-5 and top-10 retrieval results. According to the findings of the comparative study, our approach outperforms some structure and vision-based studies in the literature. With this achievement, web pages could be employed as a query item to find other, similar web pages by taking into consideration that they are web pages, instead of images or anything else.

WEB PAGE ANTICIPATION SYSTEM USING MARKOV MODEL

Web page prediction plays a vital role in anticipation and pre-fetching webpage in advance earlier than the user accesses that webpage in future, resulting in reducing the user latency and efficiently utilize the minimum bandwidth of the network. Process of accessing any information on the internet, clustering plays an important role. But clustering is done only by using the navigational behavior of the user. The clustering technique is done for the purpose of making certain groups of similar web pages. The whole process of anticipation of web page tries to use the pre-fetching techniques and Markov and hidden Markov model. On the basis of the similar behavior of web pages they are divided into various clusters and with the help of using the prediction algorithm we are able to anticipate the next most probable web page by using the user’s current accessible web page.

Effectiveness of Ant Colony Optimization for Weighted Page Rank Algorithm in Web Access

In general the web is growing very rapidly and data generation is also vast and high. Search Engines play an eminent role in retrieving data from web. The user searching for a topic in a web and it retrieves more than hundreds of searchresults as websites. Among the all websites it is difficult for the user to access all the web pages to find relevant information. Weighted Page rank algorithms play a dominant role to make navigation easier to the user. The popularity of a web page depends on the number of its in links and out links and each webpage gets a proportional page rank value. This algorithm considers only link structure not thecontent of the page, so it returns lesssignificant pages to the user query. To overcome the above issues the study focuses on Ant Colony optimization. This study proposes application of ant colony algorithm for modified weighted page rank algorithm. The ACO concept will discovery of redundant components, use clustering based on the structure similarity or web behavior for user and similar WebPages matching. User and webpage similarity matching using Ant colony Optimization based clustering will leads to better access of the webpage in less time and required webpage.

Predatory journals and dishonesty in science

Predatory magazines are created by unreliable publishers who, after collecting a fee, publish the submitted paper in the Open Access (OA) formula without providing substantive control. For the purpose of “encouraging” authors to submit their work, they often impersonate existing periodicals by using a similar-sounding title, a similar webpage, and copied names of editors of the editorial board. They also offer credits close in name to the Impact Factor. The purpose of such activity is to deceive authors and to earn money in an unfair manner. This contribution presents mechanisms used by such journals and includes suggestions for protection from the duplicity and the subsequent disappointment of publishing, often for an exorbitant fee.

Enhancing Online Similar Web Pages Advisor with Support of Text Processing

Enhancing Online Similar Web Pages Advisor with Support of Text Processing

Estimating similarity of rich internet pages using visual information

Traditional text-based web page similarity measures fail to handle rich-information-embedded modern web pages. Current approaches regard web pages as either DOM trees or images. However, the former only focuses on the web page structure, while the latter ignores the inner connections among different web page features. Therefore, they are not suitable for modern web pages. Hence, the idea of a block tree is introduced, which contains both structural and visual information of web pages. A visual similarity metric is proposed as the edit distance between two block trees. Finally, an experiment is undertaken, by cross-comparing 500 web pages, illustrating that the model appears to be highly accurate, empirically demonstrating that the metric is highly promising.

Enhanced Model of Web Page Prediction using Page Rank and Markov Model

Now-a-days, with the massive size of the web it has become hectic as well as time-consuming for the users to search for the most appropriate page in least time. Prediction of the next page saves users’ time and it becomes easy for the user to reach the most suitable or correct page. In this paper, web page prediction technique has been improved by combining clustering with markov rule and page ranking algorithm. The K-means clustering technique is used for the accumulation of the similar web pages. Page Rank Algorithm is used here to assign probabilities to web-pages from beforehand according to their importance. Markov rule has been used on each cluster to evaluate occurrences of each web pages visited under different sessions and markov model is applied to predict the next web page from the current web page. The rule of transition probability of markov model has been used to predict the next web page from the current web page.

Information-driven network analysis: evolving the “complex networks” paradigm

Network analysis views complex systems as networks with well-defined structural properties that account for their complexity. These characteristics, which include scale-free behavior, small worlds and communities, are not to be found in networks such as random graphs and lattices that do not correspond to complex systems. They provide therefore a robust ground for claiming the existence of “complex networks” as a non-trivial subset of networks. The theory of complex networks has thus been successful in making systematically explicit relevant marks of complexity in the form of structural properties, and this success is at the root of its current popularity. Much less systematic has been, on the other hand, the definition of the properties of the building components of complex networks. The obvious assumption is that these components must be nodes and links. Generally, however, the internal structure of nodes is not taken into account, and links are serendipitously identified by the perspective with which one looks at the network to be analyzed. For instance, if the nodes are Web pages that contain information about scientific papers, one point of view will match the relevant links with hyperlinks to similar Web pages, and another with citations of other articles. We intend to contribute here a systematic approach to the identification of the components of a complex network that is based on information theory. The approach hinges on some recent results arising from the convergence between the theory of complex networks and probabilistic techniques for content mining. At its core there is the idea that nodes in a complex network correspond to basic information units from which links are extracted via methods of machine learning. Hence the links themselves are viewed as emergent properties, similarly to the broader structural properties mentioned above. Indeed, beside rounding up the theory, this approach based on learning has clear practical benefits, in that it makes networks emerge from arbitrary information domains. We provide examples and applications in a variety of contexts, starting from an information-theoretic reconstruction of the well-known distinction between “strong links” and “weak links” and then delving into specific applications such as business process management and analysis of policy making.

Relation based Measuring of Semantic Similarity for Web Documents

The World Wide Web (WWW) is the information resource centre in which information exists in the structure of web pages which are interlinked with each other. From the huge amount of information present on WWW it has been found difficult to extract the relevant information for the query given by the user. The reason for this is that the information exists on web is in natural language. The layered architecture semantic web is given by Tim Berner Lee to overcome the issues of information retrieval. In recent times, numerous semantic web search engines have been developed like Ontolook, Swoogle, etc which assist in searching significant documents presented on semantic web. Several attempts have been made in ruling out the similarity of semantic web pages but then also the results of these semantic similarity techniques between web documents is neither appropriate nor upto the user’s prospects. This paper proposes an approach for finding the semantic similarity between the web documents along with the consideration of the concepts as well as the relationships that will exists between the concepts also. In our approach the documents are being processed by extracting concepts and relationships between the existing concepts from the documents using the base ontology and the dictionary having the words along with the synonyms. Finally, the set of any two documents are compared to find their semantic similarity by taking the relationships that exists in the documents. We discover all relevant relationships between the words which provide the core information of the document and then the similarity of these relationships is computed on each web page to find out their significance.

An Indexing Network: Model and Applications

Internet data are heterogeneous, redundant, disordered, and exponentially growing. Finding the right information from them becomes an ever-challenging issue. Existing technologies such as inverted index and keyword matching can list user webpage matching with given search keywords. They cannot recognize potential relations among webpages to meet some rising user needs, e.g., exploratory search and personalized search. We propose an indexing network model that organizes information in webpages at three levels: words, webpages, and categories, thereby leading to a semantic association graph. Words are used as the description of webpages and categories. Webpage classification is used to gather similar webpages together. Hyperlinks imply the wisdom of the webpage creator, which can help us generate semantic relations among categories. With a clear organizational structure, an indexing network can provide support for many important applications including intelligent information retrieval, recommendation and decision support. In order to provide access to interfaces for the proposed indexing network, an indexing network algebra is defined. Finally, to validate the proposed model, an indexing network is generated based on 30 million webpages and its structure is analyzed. We also give methods to achieve “browsing navigation” and “personalized search” based on the generated network. Results reveal that the use of an indexing network can greatly facilitate exploratory information retrieval and personalized search.

Enhancing Navigability in Websites Built Using Web Content Management Systems

Websites built using Web Content Management Systems (WCMSs) usually provide their users with three types of access structures to surf their contents: indexes of categories, breadcrumb trails, and sitemaps. In addition, to find contents of his/her interest, a user can perform more or less advanced full-text searches. In this paper we propose an automatic approach to extend the navigation structure of websites developed using WCMSs with Semantic Navigation Maps (SNMs), a complementary navigation structure that enables linking and navigating contents based on their lexical similarity. The approach uses an information retrieval technique (namely, Latent Semantic Indexing) to identify lexical similarities between textual contents, and a fuzzy clustering algorithm to form groups of similar web pages. For each page of the website, a set of navigation links towards pages showing similar content and a measure of such similarity is provided. The paper presents the approach to generate SNMs, an implementation for the Joomla! open source WCMS, and the results of an empirical evaluation involving two real world websites built using this WCMS.