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.

Research on marketing module and competitive strategy of E-Business Based on the VIP Website

E-Business now is developing fast in China and the development of E-Business strategy is crucial as the right strategy can make the E-Business select the correct direction and get a better development. In this paper, combined with SWOT parameters module, we will make a thorough research on the strategies used by Vip.eom from different angles. After this research, we will have a good understanding of strategies used by VIP website and learn to make better decisions in the e-business world.

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。

E-business在企业价值创造过程中的评价模型

电子商务作为一种崭新的商业模式,它的发展必将有利地推动传统企业经济效益的提高,对我国经济的发展产生重要的影响作用。本文从系统论与突变论的角度出发,在分析企业经济效益模型的理论基础上建立了数学模型,详细探讨了致使企业经济效益发生质变的临界点,并阐述了其作用效果与途径。

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。

浅谈企业实施E-Business系统的关键成功要素CSFs及成功标准

本文透视了E-Business的发展现状,建立了关系模型来揭示E-Business四个主要组成部分(ERP,SCM,E-Commerce和CRM)之间的关系以及它们如何为不同的战略目标服务,在大量的实际调查和现有案例研究结果的基础上,总结了E-Business系统实施的关键成功要素CSFs,并分析其对系统实施的影响,比较了ERP,SCM,E-Commerce和CRM实施的不同成功标准,旨在为企业组织实施工作和评估系统实施效果提供可靠的依据。

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.