Image Recognition Model of Fraudulent Websites Based on Image Leader Decision and Inception-V3 Transfer Learning

The fraudulent website image is a vital information carrier for telecom fraud.The efficient and precise recognition of fraudulent website images is critical to combating and dealing with fraudulent websites.Current research on image recognition of fraudulent websites is mainly carried out at the level of image feature extraction and similarity study,which have such disadvantages as difficulty in obtaining image data,insufficient image analysis,and single identification types.This study develops a model based on the entropy method for image leader decision and Inception-v3 transfer learning to address these disadvantages.The data processing part of the model uses a breadth search crawler to capture the image data.Then,the information in the images is evaluated with the entropy method,image weights are assigned,and the image leader is selected.In model training and prediction,the transfer learning of the Inception-v3 model is introduced into image recognition of fraudulent websites.Using selected image leaders to train the model,multiple types of fraudulent websites are identified with high accuracy.The experiment proves that this model has a superior accuracy in recognizing images on fraudulent websites compared to other current models.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

Multimodal Fraudulent Website Identification Method Based on Heterogeneous Model Ensemble

The feature analysis of fraudulent websites is of great significance to the combat,prevention and control of telecom fraud crimes.Aiming to address the shortcomings of existing analytical approaches,i.e.single dimension and venerability to anti-reconnaissance,this paper adopts the Stacking,the ensemble learning algorithm,combines multiple modalities such as text,image and URL,and proposes a multimodal fraudulent website identification method by ensembling heterogeneous models.Crossvalidation is first used in the training of multiple largely different base classifiers that are strong in learning,such as BERT model,residual neural network(ResNet)and logistic regression model.Classification of the text,image and URL features are then performed respectively.The results of the base classifiers are taken as the input of the meta-classifier,and the output of which is eventually used as the final identification.The study indicates that the fusion method is more effective in identifying fraudulent websites than the single-modal method,and the recall is increased by at least 1%.In addition,the deployment of the algorithm to the real Internet environment shows the improvement of the identification accuracy by at least 1.9%compared with other fusion methods.

Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

Phishing websites present a severe cybersecurity risk since they can lead to financial losses,data breaches,and user privacy violations.This study uses machine learning approaches to solve the problem of phishing website detection.Using artificial intelligence,the project aims to provide efficient techniques for locating and thwarting these dangerous websites.The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification.Logistic Regression,K-Nearest Neighbors,Decision Trees,Random Forests,Support Vector Classifiers,Linear Support Vector Classifiers,and Naive Bayes were all used in the inquiry.This research covers the benefits and drawbacks of several Machine Learning approaches,illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions.The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections.The study evaluates and compares accuracy,precision and recalls of several machine learning models in detecting phishing website URL’s detection.

Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information,a problem that persists despite user awareness.This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning(ML)models—Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories.Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%.On the other hand,LSTM shows the lowest accuracy of 96%.These findings underscore the potential of ML techniques in enhancing phishing detection systems and bolstering cybersecurity measures against evolving phishing tactics,offering a promising avenue for safeguarding sensitive information and online security.

Factors Affecting DMO’s Website Use Intention, and Visiting Intention: A SEM Model on the Impact of E-Destination Image and DMO’s Website Design

The aim of this study is to identify and analyze ways to increase usage of destination website and motivate website visitors’intention to actually visit the destination.The sample population is South Koreans who visit the VisitBritain website,the homepage of Britain’s national tourism agency.The data for this study were collected by a self-participated online survey on the blog of VisitBritain Korea,which is directly linked to the VisitBritain Korea website.Using structural equation modeling,six hypotheses were tested.The results show that the destination image on the destination marketing organization(DMO)website,quality of the destination website design including three dimensions(information quality,system quality,and service quality),and customers’satisfaction formed by high quality of the website design have a significant impact on website visitors’use intention and increase their visiting intention to the destination.Based upon the results of the analysis,managerial implications and areas for future research are proposed.

基于Website信息发布平台上的二次开发

介绍Website信息发布平台的基本功能 ,并且将利用该平台建立Web站点与传统方法建立Web站点相比较 ;

基于Silverlight+Websites的管理技术方案研究

Silverlight开发平台具有灵活的编程,良好的用户体验,强大的功能特点。Websites把GIS和Web有机结合,是因特网技术应用于GIS开发的产物,Web对GIS实现了扩展和完善。基于Silverlight+Websites的研究,具有良好的扩展性和交互性,应用前景广阔。本文从Silverlight开发与分析、Auto CAD和Websites的关键技术、基于Silverlight+Websites开发技术路线、管理建模四个方面进行了详细的探讨。

Evaluation of the Trading Website for Agricultural Products in China

Agricultural product trading website is not only an important way to realize the agriculture informatization,but also the main manifestation of the agricultural informatization. Based on the preliminary understanding of the content and characteristics of China's agricultural product trading website,the paper builds a scientific evaluation indicator system and evaluates 50 typical agricultural product trading websites objectively by using classification and grading method. The results show that the overall construction level of China's agricultural product trading websites is general,and there are obvious differences between regions; the lack of website commercial function and the lag of informatization are the main factors restricting the development of agricultural product trading websites.

Phishing Websites Detection by Using Optimized Stacking Ensemble Model

Phishing attacks are security attacks that do not affect only individuals’or organizations’websites but may affect Internet of Things(IoT)devices and net-works.IoT environment is an exposed environment for such attacks.Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users.Machine and deep learning and other methods were used to design detection methods for these attacks.However,there is still a need to enhance detection accuracy.Optimization of an ensemble classification method for phishing website(PW)detection is proposed in this study.A Genetic Algo-rithm(GA)was used for the proposed method optimization by tuning several ensemble Machine Learning(ML)methods parameters,including Random Forest(RF),AdaBoost(AB),XGBoost(XGB),Bagging(BA),GradientBoost(GB),and LightGBM(LGBM).These were accomplished by ranking the optimized classi-fiers to pick out the best classifiers as a base for the proposed method.A PW data-set that is made up of 4898 PWs and 6157 legitimate websites(LWs)was used for this study's experiments.As a result,detection accuracy was enhanced and reached 97.16 percent.

Comprehensiveness and Locality: Website Marketing of World City Governments in the Era of Globalisation

In the era of the Internet and globalisation, more and more international academics focus their attention on how city governments compete for talent, capital, and technology through website marketing to promote their economy and global status. However, 1) present research generally overlooks the importance of different types of elements in different marketing themes, 2) the combinations of marketing themes are still unknown, and 3) the presumption that the emphasised elements and specific combination of marketing themes on official websites differentiates cities requires more cases to be understood. In light of this background, this study collects homepage elements of 49 Alpha world cities' official websites and quantitatively analyses the frequency of different types of elements, the marketing content themes, and the dissimilarity of content of Chinese Alpha world cities. The results indicate that comprehensiveness and locality appear in the process of city marketing throughout official city websites. Overall, we make the following conclusions. 1) The importance of different kinds of elements significantly differs between 49 Alpha world cities. 2) Based on various combinations of elements, the marketing contents of Alpha world cities through official websites can be categorised into six themes of history and culture, government and information, construction and environment, government and living, construction and living, and general compound. 3) The marketing elements of five Chinese Alpha world cities, including Hong Kong, Beijing, Shanghai, Taipei and Guangzhou, are different than the other 44 Alpha world cities, and Chinese cities prefer to advertise their history and culture but rarely market citizens' activities. Moreover, Chinese cities' marketing mostly targets natives while the other 44 Alpha cities target external groups, and the locality of world cities' website marketing is reinforced especially on a native language edition website. This study ultimately finds that the Chinese edition websites of five Chinese cities place more focus on introducing local historical buildings, administrative services, and internal business information than the English edition websites do.

Information Network Systems and Information Sharing on Administrative Websites

In Japanese 'e-government' policy, called 'e-Japan', the 'administrative document management system' is functioning as information searching systems. On the other hand, this system has also generated the problem that it is not fully functioning as a means for the information sharing in a governmental agency. So, the purpose of this research is to find how the administrative document management system can function as information sharing in administrative organization. For this purpose, this paper considers the current status and some problems firstly. And secondary, this paper proposes the idea and constructs some information systems using administrative official Website. This is the method and approach of this research. As a conclusion, this proposal information system junctions as information sharing support systems.

Machine Learning-Based Advertisement Banner Identification Technique for Effective Piracy Website Detection Process

In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement.Billions of dollars are lost annually because of this illegal act. The currentmost effective trend to tackle this problem is believed to be blocking thosewebsites, particularly through affiliated government bodies. To do so, aneffective detection mechanism is a necessary first step. Some researchers haveused various approaches to analyze the possible common features of suspectedpiracy websites. For instance, most of these websites serve online advertisement, which is considered as their main source of revenue. In addition, theseadvertisements have some common attributes that make them unique ascompared to advertisements posted on normal or legitimate websites. Theyusually encompass keywords such as click-words (words that redirect to installmalicious software) and frequently used words in illegal gambling, illegal sexual acts, and so on. This makes them ideal to be used as one of the key featuresin the process of successfully detecting websites involved in the act of copyrightinfringement. Research has been conducted to identify advertisements servedon suspected piracy websites. However, these studies use a static approachthat relies mainly on manual scanning for the aforementioned keywords. Thisbrings with it some limitations, particularly in coping with the dynamic andever-changing behavior of advertisements posted on these websites. Therefore,we propose a technique that can continuously fine-tune itself and is intelligentenough to effectively identify advertisement (Ad) banners extracted fromsuspected piracy websites. We have done this by leveraging the power ofmachine learning algorithms, particularly the support vector machine with theword2vec word-embedding model. After applying the proposed technique to1015 Ad banners collected from 98 suspected piracy websites and 90 normal orlegitimate websites, we were able to successfully identify Ad banners extractedfrom suspected piracy websites with an accuracy of 97%. We present thistechnique with the hope that it will be a useful tool for various effective piracywebsite detection approaches. To our knowledge, this is the first approachthat uses machine learning to identify Ad banners served on suspected piracywebsites.

Analyzing the activities of visitors of the Leiden Ranking website

Purpose： To get a better understanding of the way in which university rankings are used.Design/methodology/approach： Detailed analysis of the activities of visitors of the website of the CWTS Leiden Ranking.Findings： Visitors of the Leiden Ranking website originate disproportionally from specific countries. They are more interested in impact indicators than in collaboration indicators, while they are about equally interested in size-dependent indicators and size-independent indicators. Many visitors do not seem to realize that they should decide themselves which criterion they consider most appropriate for ranking universities.Research limitations： The analysis is restricted to the website of a single university ranking. Moreover, the analysis does not provide any detailed insights into the motivations of visitors of university ranking websites.Practical implications： The Leiden Ranking website may need to be improved in order to make more clear to visitors that they should decide themselves which criterion they want to use for ranking universities.Originality/value： This is the first analysis of the activities of visitors of a university ranking website.

Evaluation of Cross-Strait Public Library Website

In recent years, many evaluations on Web sites have been conducted, and relevant researches have also been carried out in academic circle. This paper introduces an innovative method called correspondence analysis to evaluate public library Websites from five aspects-traffic, visit, connectivity, speed and page views. Some suggestions based on the result of evaluation are given in this paper with an expectation to promote the construction and development of public library Websites.

Learning Association Rules and Tracking the Changing Concepts on Webpages:An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category(pornographic or normal) and the critical features.Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.

Documental Quality of Websites Concerning Physical Activity, Lifestyle and Sedentarism Available on the Internet

Objectives: To analyze the documental quality of 389 websites in Portuguese about physical activity, healthy lifestyles and sedentary lifestyles found on the Brazilian version of the general search engine Google. Methods: The documental quality of the 389 websites was estimated based upon the following parameters: 1) a combination of quality criteria from the Health Information Locator (LIS—OPS/BIREME) and those from Chile’s Pontifical Catholic University, organized into 17 variables;2) uniformity of reference criteria (Vancouver);3) association between the presence of authorship and a higher number of the quality criteria being fulfilled. We also studied the ranking of the results presented by Google in addition to attributes connected to the websites’ target audience, the types of content, their sponsors and country of origin. Results: Of the 389 websites studied, 111 links were not active (28.53% CI 95% [24.05 - 33.02]) and none of the websites in the sample met all of the 17 quality variables. Authored websites displayed remarkable differences in quality when compared to those which did not identify their authors. Conclusions: Faced with the issue of the proliferation of websites with questionable quality content, and the fact that the ranking of results interferes directly in the internal evaluation of content relevance, we propose that public-health research institutions cooperate with web-searching developers to improve the website-positioning formula, in which the “identified authorship” criterion should play a major role in the ranking system.

Accuracy of Ebola Information in a Knowledge Exchange Social Website (KESW)

Background: Misinformation on interactive Knowledge Exchange Social Websites (KESWs) is concerning since it can influence Internet users’ health behaviors, especially during an infectious disease outbreak. Objective: The present study seeks to examine the accuracy and characteristics of health information posted to a Knowledge Exchange Social Website (KESW). Methods: A sample of 204 answers to Ebola questions were extracted and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosed, link, and word count) predicted accuracy. Results: Overall, only 27.0% of the posted answers were rated as “accurate”. Accuracy varied across question topics with between 11.8% - 45.5% of answers being rated as accurate. When Yahoo Answers’ “best answers” were examined, the overall accuracy was substantially higher, with 80.0% of “best answers” being rated as accurate compared to 16.0% of all other answers. Conclusion: There is need for tools to help Internet users navigate health information posted on these dynamic user-generated knowledge exchange social websites.

A General Framework For Determining the Temporal and Evolutionary Dynamics of Religion-Based Website Popularity on the Internet

Religion-based websites are fast becoming a major pipeline for disseminating religious information to broad populations of individuals in the United States.Both mainstream religions and fringe religions are easily accessible to a large population of internet users. The purpose of this review is to develop and examine a general framework that uses simple mathematical and statistical models to interpret and measure temporal ‘snap shots’ in the popularity of religious websites. We extend this framework to include an evolutionary model that has the potential to predict long-term shifts or changes in the popularity of religious websites over time. Ultimately, the goal of this review is to introduce a new modeling framework for research into how the internet is changing the accessibility and views of populations of individuals who follow various religions on the internet and how this may in-turn affect the distribution of religion in the ‘real world’.