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.

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Website的旅行社电子商务运营绩效影响因素研究——以四川省为例

此文在文献研究的基础上,归纳了旅行社网站绩效的评价因子,并提出若干影响旅行社电子商务运营绩效的因素,经相关性检验证实了各项影响因素设计的合理性及其对旅行社网站绩效的影响强度,最后,通过聚类分析获得影响因素的分类特征。相关成果将为促进我国旅行社电子商务的理论研究和实践运作提供有益参考。

Algorithms of mining data records from website automatically

In order to improve the accuracy and integrality of mining data records from the web, the concepts of isomorphic page and directory page and three algorithms are proposed. An isomorphic web page is a set of web pages that have uniform structure, only differing in main information. A web page which contains many links that link to isomorphic web pages is called a directory page. Algorithm 1 can find directory web pages in a web using adjacent links similar analysis method. It first sorts the link, and then counts the links in each directory. If the count is greater than a given valve then finds the similar sub-page links in the directory and gives the results. A function for an isomorphic web page judgment is also proposed. Algorithm 2 can mine data records from an isomorphic page using a noise information filter. It is based on the fact that the noise information is the same in two isomorphic pages, only the main information is different. Algorithm 3 can mine data records from an entire website using the technology of spider. The experiment shows that the proposed algorithms can mine data records more intactly than the existing algorithms. Mining data records from isomorphic pages is an efficient method.

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。

从The Great Websites for Kids看儿童网络资源的组织管理与评价

从机构组织、评价流程、评价标准、资源组织等方面,对美国最大的儿童网络资源导航The Great Websites for Kids(GWS)进行评述,为我国儿童网络资源的建设、管理、评价和推介提供参考。

On The Evaluation of a Useful Website and Its Application to an English Lesson

随着多媒体技术的运用和因特网的问世,计算机在语言教学中的作用日益被世界各国的语言教学者和学习者所重视。万维网(WWW)因其拥有无数英语学习网站而逐渐被人们广泛地运用于语言教学,然而,面对如此众多纷呈的学习网站,人们往往难以区分良莠。本文试图通过评价某一优秀网站,并示范如何应用该网站资源于英语课堂教学,为广大的语言教学者和学习者提出选择合适网站的方法与建议,以起到“抛砖引玉”的作用。

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.

User Behaviors Analysis in Website Identification Registration

Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese Software Develop Net (CSDN) to provide a complete perspective on this research point. We concentrate on the following three aspects: complexity, correlation, and preference. From these analyses, we draw the following conclusions: firstly, a considerable number of users have not realized the importance of identification and are using very simple identifications that can be attacked very easily. Secondly, there is a strong complexity correlation among the three parts of identification. Thirdly, the top three passwords that users like are 123456789, 12345678 and 11111111, and the top three email providers that they prefer are NETEASE, qq and sina. Further, we provide some suggestions to improve the quality of user passwords.

Quality Diagnosis of BtoC Website Based on Fuzzy Comprehensive Evaluation

A hl-quality website is crucial to a company for a successful e-business. The technique maintainers are always faced with the problem how to locate the prime factors which affect the quality of the websites. In view of the complexity and fuzziness of BtoC webslte, a quality diagnosis method based on the multl-attribute and multi-layer fuzzy comprehensive evaluation model including all the quality factors is proposed. A simple example of diagnosis on a famous domestic BtoC websites shows the specific steps of this method and proves its validity. The process of quality evaluation and diagnosis system is illustrated and the computer program of diagnosis is Oven.

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.

A genre analysis of the B2B website

The advent of the Intemet has witnessed a revolution in the business world. One typical example is the emergence of the B2B website. The present paper looks at the B2B website, a conventionalized digital text, in terms of its communicative purposes, move features as well as linguistic specialties, with the aim of presenting the generic structure of the B2B website and its principal linguistic features contributing to the realization of its communicative purposes. It is demonstrated that the B2B website is one instance of the promotional genres and it has a lot in common with advertisement English and ＂netsneak＂ in the aspect of lexico-grammatical features.

Research on Big Data and Artificial Intelligence Aided Decision-Making Mechanism with the Applications on Video Website Homemade Program Innovation

In this paper, we conduct research on the big data and the artificial intelligence aided decision-making mechanism with the applications on video website homemade program innovation. Make homemade video shows new media platform site content production with new possible, as also make the traditional media found in Internet age, the breakthrough point of the times. Site homemade video program, which is beneficial to reduce copyright purchase demand, reduce the cost, avoid the homogeneity competition, rich advertising marketing at the same time, improve the profit pattern, the organic combination of content production and operation, complete the strategic transformation. On the basis of these advantages, once the site of homemade video program to form a brand and a higher brand influence. Our later research provides the literature survey for the related issues.

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.