scholarly journals A Detection Method for Phishing Web Page Using DOM-Based Doc2Vec Model

2020 ◽  
Vol 28 (1) ◽  
pp. 19-31
Author(s):  
Jian Feng ◽  
Ying Zhang ◽  
Yuqiang Qiao
Keyword(s):  
Semantic Information ◽  
Detection Method ◽  
Structural Characteristics ◽  
Web Pages ◽  
Web Page ◽  
Clustering Method ◽  
Semantic Clustering ◽  
Dom Tree ◽  
Structural Semantics ◽  
Linguistic Approach

Detecting phishing web pages is a challenging task. The existing detection method for phishing web page based on DOM (Document Object Model) is mainly aiming at obtaining structural characteristics but ignores the overall representation of web pages and the semantic information that HTML tags may have. This paper regards DOMs as a natural language with Doc2Vec model and learns the structural semantics automatically to detect phishing web pages. Firstly, the DOM structure of the obtained web page is parsed to construct the DOM tree, then the Doc2Vec model is used to vectorize the DOM tree, and to measure the semantic similarity in web pages by the distance between different DOM vectors. Finally, the hierarchical clustering method is used to implement clustering of web pages. Experiments show that the method proposed in the paper achieves higher recall and precision for phishing classification, compared to DOM-based structural clustering method and TF-IDF-based semantic clustering method. The result shows that using Paragraph Vector is effective on DOM in a linguistic approach.

Semantic Question Answering Using Wikipedia Categories Clustering

2014 ◽  
Vol 23 (04) ◽  
pp. 1460014 ◽  
Author(s):  
Georgios Stratogiannis ◽  
Georgios Siolas ◽  
Andreas Stafylopatis
Keyword(s):  
Information Retrieval ◽  
Linear Combination ◽  
Search Engine ◽  
Noun Phrase ◽  
Question Answering ◽  
Semantic Information ◽  
Web Pages ◽  
Clustering Method ◽  
Information Retrieval Methods ◽  
The Ideal

We describe a system that performs semantic Question Answering based on the combination of classic Information Retrieval methods with semantic ones. First, we use a search engine to gather web pages and then apply a noun phrase extractor to extract all the candidate answer entities from them. Candidate entities are ranked using a linear combination of two IR measures to pick the most relevant ones. For each one of the top ranked candidate entities we find the corresponding Wikipedia page. We then propose a novel way to exploit Semantic Information contained in the structure of Wikipedia. A vector is built for every entity from Wikipedia category names by splitting and lemmatizing the words that form them. These vectors maintain Semantic Information in the sense that we are given the ability to measure semantic closeness between the entities. Based on this, we apply an intelligent clustering method to the candidate entities and show that candidate entities in the biggest cluster are the most semantically related to the ideal answers to the query. Results on the topics of the TREC 2009 Related Entity Finding task dataset show promising performance.

scholarly journals Webpage Recommendation System Based on the Social Media Semantic Details of the Website

2021 ◽  
Vol 12 (6) ◽  
pp. 237-243
Author(s):  
Dr. R.Rooba Et.al
Keyword(s):  
Recommendation System ◽  
Semantic Information ◽  
Markov Chain Model ◽  
Similarity Score ◽  
Web Pages ◽  
Chain Model ◽  
Web Page ◽  
The Social ◽  
Recommendation Accuracy ◽  
The Web

The web page recommendation is generated by using the navigational history from web server log files. Semantic Variable Length Markov Chain Model (SVLMC) is a web page recommendation system used to generate recommendation by combining a higher order Markov model with rich semantic data. The problem of state space complexity and time complexity in SVLMC was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) and Support vector machine based SVLCPMC (SSVLCPMC) meth-ods respectively. The recommendation accuracy was further improved by quickest change detection using Kullback-Leibler Divergence method. In this paper, socio semantic information is included with the similarity score which improves the recommendation accuracy. The social information from the social websites such as twitter is considered for web page recommendation. Initially number of web pages is collected and the similari-ty between web pages is computed by comparing their semantic information. The term frequency and inverse document frequency (tf-idf) is used to produce a composite weight, the most important terms in the web pages are extracted. Then the Pointwise Mutual Information (PMI) between the most important terms and the terms in the twitter dataset are calculated. The PMI metric measures the closeness between the twitter terms and the most important terms in the web pages. Then this measure is added with the similarity score matrix to provide the socio semantic search information for recommendation generation. The experimental results show that the pro-posed method has better performance in terms of prediction accuracy, precision, F1 measure, R measure and coverage.

Capturing Semantics of Web Page using Weighted TAG- Tree for Information Retrieval

2012 ◽  
Vol 3 (4) ◽  
pp. 7-24 ◽  
Author(s):  
R. Vishnu Priya ◽  
A. Vadivel
Keyword(s):  
Semantic Information ◽  
Retrieval System ◽  
Experimental Result ◽  
Web Pages ◽  
Web Page ◽  
Tree Form ◽  
Search Results ◽  
User Query ◽  
Interactive Mining ◽  
Ranking Mechanism

Web pages are highly dynamic and it’s difficult to retrieve the relevant web pages in top 10 search results. This is based on some ranking mechanism incorporated retrieval system. The Retrieval system is designed for ranking the relevant web pages for user query. Usually, the retrieval system considers many techniques for ranking such as link based, connectivity based and keyword based techniques. The authors’ rank the web pages using the keywords and its associated TAGs. Based on the importance of each TAGs, weights are assigned and the semantics of the page is captured. In addition, the semantic information is represented in compact tree form, which supports both incremental and interactive mining with refined retrieval. From the experimental result, the authors have observed that the performance of the proposed approach is encouraging compared to the recently proposed approach.

scholarly journals Exploring website gist through rapid serial visual presentation

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Justin W. Owens ◽  
Barbara S. Chaparro ◽  
Evan M. Palmer
Keyword(s):  
Rapid Serial Visual Presentation ◽  
Semantic Information ◽  
Conceptual Models ◽  
Scene Perception ◽  
Visual Presentation ◽  
Natural Scenes ◽  
Web Pages ◽  
Stimulus Exposure ◽  
Web Page ◽  
Single Fixation

Abstract Background Users can make judgments about web pages in a glance. Little research has explored what semantic information can be extracted from a web page within a single fixation or what mental representations users have of web pages, but the scene perception literature provides a framework for understanding how viewers can extract and represent diverse semantic information from scenes in a glance. The purpose of this research was (1) to explore whether semantic information about a web page could be extracted within a single fixation and (2) to explore the effects of size and resolution on extracting this information. Using a rapid serial visual presentation (RSVP) paradigm, Experiment 1 explored whether certain semantic categories of websites (i.e., news, search, shopping, and social networks/blogs) could be detected within a RSVP stream of web page stimuli. Natural scenes, which have been shown to be detectable within a single fixation in the literature, served as a baseline for comparison. Experiment 2 examined the effects of stimulus size and resolution on observers’ ability to detect the presence of website categories using similar methods. Results Findings from this research demonstrate that users have conceptual models of websites that allow detection of web pages from a fixation’s worth of stimulus exposure, when provided additional time for processing. For website categories other than search, detection performance decreased significantly when web elements were no longer discernible due to decreases in size and/or resolution. The implications of this research are that website conceptual models rely more on page elements and less on the spatial relationship between these elements. Conclusions Participants can detect websites accurately when they were displayed for less than a fixation and when the participants were allowed additional processing time. Subjective comments and stimulus onset asynchrony data suggested that participants likely relied on local features for the detection of website targets for several website categories. This notion was supported when the size and/or resolution of stimuli were decreased to the extent that web elements were indistinguishable. This demonstrates that schemas or conceptualizations of websites provided information sufficient to detect websites from approximately 140 ms of stimulus exposure.

Logical Structure for User Friendly dynamic Web Page Visualization for Small Screen Terminals Promoting E-Business

2020 ◽  
Vol 14 ◽  
Author(s):  
Shefali Singhal ◽  
Poonam Tanwar
Keyword(s):  
Logical Structure ◽  
Vital Role ◽  
Web Pages ◽  
Main Concern ◽  
Web Page ◽  
Large Screen ◽  
Tree Data ◽  
Tree Data Structure ◽  
User Friendly ◽  
Small Screen

Abstract:: Now-a-days when everything is going digitalized, internet and web plays a vital role in everyone’s life. When one has to ask something or has any online task to perform, one has to use internet to access relevant web-pages throughout. These web-pages are mainly designed for large screen terminals. But due to mobility, handy and economic reasons most of the persons are using small screen terminals (SST) like mobile phone, palmtop, pagers, tablet computers and many more. Reading a web page which is actually designed for large screen terminal on a small screen is time consuming and cumbersome task because there are many irrelevant content parts which are to be scrolled or there are advertisements, etc. Here main concern is e-business users. To overcome such issues the source code of a web page is organized in tree data-structure. In this paper we are arranging each and every main heading as a root node and all the content of this heading as a child node of the logical structure. Using this structure, we regenerate a web-page automatically according to SST size. Background:: DOM and VIPS algorithms are the main background techniques which are supporting the current research. Objective:: To restructure a web page in a more user friendly and content presenting format. Method Backtracking:: Method Backtracking: Results:: web page heading queue generation. Conclusion:: Concept of logical structure supports every SST.

Automatic Ontology Learning from Multiple Knowledge Sources of Text

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
B Sathiya ◽  
T.V. Geetha
Keyword(s):  
Knowledge Base ◽  
Web Pages ◽  
Knowledge Sources ◽  
Statistical Measure ◽  
Ontology Learning ◽  
Web Page ◽  
Semantic Query ◽  
Probabilistic Knowledge ◽  
Discovery Algorithms ◽  
Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

Semi-Automatic Online Tagging with K-Medoid Clustering

2014 ◽  
Vol 24 (08) ◽  
pp. 1115-1130 ◽  
Author(s):  
He Hu ◽  
Xiaoyong Du
Keyword(s):  
Clustering Algorithm ◽  
Prototype System ◽  
Web Pages ◽  
Web Page ◽  
Web Browser ◽  
User Input ◽  
Browser Extension ◽  
Annotation Process ◽  
Efficiency And Effectiveness ◽  
Automatic Mechanism

Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can automatically group similar HTML DOM nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios.

scholarly journals A Simulation of the Structure of the World-Wide Web

2002 ◽  
Vol 7 (1) ◽  
pp. 9-25 ◽  
Author(s):  
Moses Boudourides ◽  
Gerasimos Antypas
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Web Sites ◽  
World Wide ◽  
The Internet ◽  
Web Pages ◽  
Small Worlds ◽  
Web Page ◽  
Simple Simulation ◽  
The World

In this paper we are presenting a simple simulation of the Internet World-Wide Web, where one observes the appearance of web pages belonging to different web sites, covering a number of different thematic topics and possessing links to other web pages. The goal of our simulation is to reproduce the form of the observed World-Wide Web and of its growth, using a small number of simple assumptions. In our simulation, existing web pages may generate new ones as follows: First, each web page is equipped with a topic concerning its contents. Second, links between web pages are established according to common topics. Next, new web pages may be randomly generated and subsequently they might be equipped with a topic and be assigned to web sites. By repeated iterations of these rules, our simulation appears to exhibit the observed structure of the World-Wide Web and, in particular, a power law type of growth. In order to visualise the network of web pages, we have followed N. Gilbert's (1997) methodology of scientometric simulation, assuming that web pages can be represented by points in the plane. Furthermore, the simulated graph is found to possess the property of small worlds, as it is the case with a large number of other complex networks.

Exploring the customer orientation of Spanish pharmacy websites

2018 ◽  
Vol 12 (4) ◽  
pp. 447-462 ◽  
Author(s):  
Carmen Domínguez-Falcón ◽  
Domingo Verano-Tacoronte ◽  
Marta Suárez-Fuentes
Keyword(s):  
Web 2.0 ◽  
Customer Orientation ◽  
Community Pharmacies ◽  
Web Pages ◽  
Web Page ◽  
Pharmaceutical Sector ◽  
Content Type ◽  
Web Page Design ◽  
Page Design ◽  
The Web

Purpose The strong regulation of the Spanish pharmaceutical sector encourages pharmacies to modify their business model, giving the customer a more relevant role by integrating 2.0 tools. However, the study of the implementation of these tools is still quite limited, especially in terms of a customer-oriented web page design. This paper aims to analyze the online presence of Spanish community pharmacies by studying the profile of their web pages to classify them by their degree of customer orientation. Design/methodology/approach In total, 710 community pharmacies were analyzed, of which 160 had Web pages. Using items drawn from the literature, content analysis was performed to evaluate the presence of these items on the web pages. Then, after analyzing the scores on the items, a cluster analysis was conducted to classify the pharmacies according to the degree of development of their online customer orientation strategy. Findings The number of pharmacies with a web page is quite low. The development of these websites is limited, and they have a more informational than relational role. The statistical analysis allows to classify the pharmacies in four groups according to their level of development Practical implications Pharmacists should make incremental use of their websites to facilitate real two-way communication with customers and other stakeholders to maintain a relationship with them by having incorporated the Web 2.0 and social media (SM) platforms. Originality/value This study analyses, from a marketing perspective, the degree of Web 2.0 adoption and the characteristics of the websites, in terms of aiding communication and interaction with customers in the Spanish pharmaceutical sector.

scholarly journals A Web Page Clustering Method Based on Formal Concept Analysis

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 228 ◽  
Author(s):  
Zuping Zhang ◽  
Jing Zhao ◽  
Xiping Yan
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Web Pages ◽  
Web Page ◽  
Data Links ◽  
Web Page Clustering ◽  
The Web

Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect.

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