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.

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.

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.

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.

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.

Behind HumanBoost: Analysis of Users’ Trust Decision Patterns for Identifying Fraudulent Websites

This paper analyzes users’ trust decision patterns for detecting phishing sites. Our previous work proposed HumanBoost [1] which improves the accuracy of detecting phishing sites by using users’ Past Trust Decisions (PTDs). Web users are generally required to make trust decisions whenever their personal information is requested by a website. Human-Boostassumed that a database of Web user’s PTD would be transformed into a binary vector, representing phishing or not-phishing, and the binary vector can be used for detecting phishing sites, similar to the existing heuristics. Here, this paper explores the types of the users whose PTDs are useful by running a subject experiment, where 309 participants- browsed 40 websites, judged whether the site appeared to be a phishing site, and described the criterion while assessing the credibility of the site. Based on the result of the experiment, this paper classifies the participants into eight groups by clustering approach and evaluates the detection accuracy for each group. It then clarifies the types of the users who can make suitable trust decisions for HumanBoost.

基于OBE的地方院校GIS专业线上课程资源网站建设探索

OBE理念重视学生通过学习取得成果与进步,为了丰富学生的系统学习资源和提升学生的实践创新本领,以嘉应学院地理信息科学(GIS)专业为例,探索地方院校基于OBE理念的线上课程资源网站建设.该课程资源网站建设以专业知识、技术工具、综合能力、价值情感和实践应用这5个维度课程目标为导向,构建课程学习共同体和学生深度参与网站建设的创新教学方式体系.师生共同组织学习活动,将课堂教学、线上学习与第二课堂有机结合,以达到培养创新应用型GIS人才的目标.

基于文本⁃视觉多特征融合的非法网站识别研究

当前非法网站存在隐蔽性强、危害性高的特点,仅依赖单一特征的网站识别方法无法有效应对这种复杂性。针对上述问题,文中提出一种基于文本⁃视觉多特征融合的非法网站识别方法。首先构建基于ResNet⁃18的视觉特征提取模型和基于BERT⁃CNN的文本特征提取模型;然后通过设计的基于逻辑回归(LR)的融合算法对两种模型的分类结果进行融合;最后通过多轮次迭代训练得出最佳的非法网站判别模型。实验结果表明,文中构建的融合模型相较于依赖文本和视觉的单一特征模型的准确率分别高出4%和11%,能够更准确地识别非法网站。

基于多维特征的涉诈网站检测与分类技术研究

随着互联网的发展与普及,涉诈团伙诈骗手法与反检测技术愈发先进,涉诈网站的检测与分类对于网络空间安全重要性更加显著,而传统的检测技术已无法应对现在的新型诈骗网站,并且针对涉诈网站分类的研究很少.针对此热点难题,本文分析了当今新型涉诈网站的多个典型特征并提出了一种基于多维特征的涉诈网站检测与分类系统.该系统共构建11种涉诈网站特征与3600个网页关键词来表示一个涉诈网站.系统首先利用爬虫获取待检测域名的网页截图、WHOIS信息与源码并交给特征抽取模块构建多维特征集.检测模块提取网站域名、代码结构以及网站WHOIS信息作为特征,构建随机森林模型实现检测任务.然后基于检测结果,网页分类模块利用双向GRU提取网页的文本特征,在置信度小于0.7的情况下使用BERT模型从而保证系统准确度与效率,并使用残差神经网络提取网页截图特征,同时计算网页内部图片与网站Logo相似度,创建随机森林模型进行分类,并设计了对比实验进一步分析模型的准确性.实验证明,本文提出的模型拥有很高的准确性,模型平均F1-score达到97.28%.实验结果表明,本文提出的多维特征模型能很好地区分涉诈网站与正常网站,克服了传统方法应对新型涉诈网站的识别问题,并适用于全球新增域名的涉诈网站快速检测与分类.

智慧校园学习交流与交易网站设计与实现

随着科技的进步和人们对于教育的愈发重视,智慧校园已成为教育领域的热门话题。基于此,对基于Vue和Spring Boot的大学智慧校园学习交流与交易网站进行了研究,通过IDEA、Visual Studio Code开发工具实现前后端数据交互。该网站分为学习区、跳蚤市场区和生活交流区等多个分区。用户可以在网站上发布帖子,进行交流和交易。该网站为大学生提供一个方便、快捷、安全、可信的校园互动平台。

基于机器学习算法的网站维护告警识别系统设计与实现

本论文旨在设计并实现一种基于机器学习算法的网站维护告警识别系统,以解决当前网站管理中存在的数据离散问题,提高管理效率和效果。针对网站维护过程中出现的大量告警信息,文章提出了一套告警日志分析识别系统,通过有效地从无用告警信息中提取出重要的告警信息,实现对网站维护者的实时预警。在系统架构设计中,本研究使用了决策树(Decision Trees,DT)、支持向量机(Support Vector Machine,SVM)和梯度提升树(Gradient Boosting Tree,GBT)等机器学习算法对告警日志进行分析和建模,并通过实验验证了梯度提升树模型在识别有效告警方面的最佳性能。最终选择的系统能够帮助网站管理者及时发现和处理重要的系统异常或错误事件,提高网站的稳定性和可靠性。

电信网络诈骗犯罪防治研究综述

随着互联网和人工智能的发展,电信网络诈骗犯罪处于高发态势,严重危害人民群众的财产安全。电信网络诈骗犯罪的防范和治理不仅受到了国家和学者的重视,而且成为了网络安全研究热点。首先介绍了基于诈骗电话、诈骗信息数据的电信网络诈骗防范研究现状;其次介绍了关于诈骗网站和恶意网站识别的研究现状;最后基于目前电信网络诈骗犯罪预防的需求,分析深度学习和大语言模型性能优势,对未来诈骗网站识别的可研究方向进行了探讨和展望。

The Pedagogical,Linguistic,and Content Features of Popular English Language Learning Websites in China:A Framework for Analysis and Design

As increasing numbers of Chinese language learners choose to learn English online,there is a need to investigate popular websites and their language learning designs.This paper reports on the first stage of a study that analyzed the pedagogical,linguistic,and content features of 25 Chinese English Language Learning(ELL)websites ranked according to their value and importance to users.The website ranking was undertaken using a system known as PageRank.The aim of the study was to identify the features characterizing popular sites as opposed to those of less popular sites for the purpose of producing a framework for ELL website design in the Chinese context.The study found that a pedagogical focus with developmental instructional materials accommodating diverse proficiency levels was a major contributor to website popularity.Chinese language use for translations and teaching directives and intermediate level English for learning materials were also significant features. Content topics included Anglophone/Western and non-Anglophone/Eastern contexts. Overall, popular websites were distinguished by their mediation of access to and scaffolded support for ELL.

“产、赛、学”融合下Web前端技术课程建设改革探索

针对当前本科院校Web前端开发技术课程教学中存在的教学目标不明确、专业实践能力培养不足、教学过程设计与考评机制不完善等问题,该文基于产学合作协同育人项目指南,探索融合“产、赛、学”为一体的Web前端开发技术课程建设改革重点、策略及有效教学措施,以期为其他院校相关课程建设或教学改革提供借鉴。

基于Java的RFID课程学习网站设计与实现

随着疫情的发展,线上学习逐步成为趋势。基于信息技术搭建在线学习平台,可以让学生更方便地学习到知识。在这样的背景下,文章提出基于B/S架构的RFID课程学习系统。该系统不仅能够支持管理员在后台自定义各种课程,同时还允许用户结合自己的需求进行个性化的学习,应用前景十分广阔。

基于多开发特征的风险网站发现

如今涉诈风险网站处于高发阶段,风险站发现面临巨大的挑战,而现有的风险网站发现技术暴露出很大的局限性。基于此,论文提出了一种基于多开发特征的风险网站发现方法。论文方法在分析提取URL、HTML、JavaScript(JS)代码特征基础之上,进一步引入Cascading Style Sheets(CSS)代码特征,充分提取网站中多类代码特征用于风险网站识别,有效提高了风险网站发现的精确度。首先,通过爬取整个网页的所有信息,先提取到网页内容中的HTML完整代码信息,再根据内部涵盖的路径信息可分别获取JS和CSS代码。其次,借助于这些代码并进一步抽取代码特征,使用机器学习的方法可以有效区别网站类型。实验发现随机森林(Random Forest,RF)有在方法上表现出更好的分类效果,其准确率和召回率分别为98.3%和98.3%,高于相关工作实验结果。

基于Res-CAN的Tor网站指纹识别模型

网站指纹识别技术通过分析流量特征判断用户访问的网站站点,能够有效监管TOR匿名网络的用户行为。现有的识别方法通常需要大规模的数据样本以获得高的识别准确率,且普遍存在概念漂移问题。针对以上问题,本文提出一种基于残差和协作对抗网络(Residual network and Collaborative and Adversarial Network,Re s-CAN)的网站指纹识别模型。该模型使用残差网络(Residual network)作为特征提取器以减少网络的优化难度。同时,将协作对抗网络(Collaborative and Adversarial Network,CAN)应用于网站指纹识别问题,使得特征提取器同时学习领域相关和领域无关特征,实现源域与目标域的特征空间对齐。实验结果表明,本文提出的方法在小样本环境下网站指纹识别准确率达到91.2%,优于现有的利用对抗领域自适应网络(Domain-Adversarial Neural Networks,DANN)迁移学习方法,且抗概念漂移能力较高。

中职学校学习网站的设计及应用效果研究

中职教育担负着为国家和社会培养技术人才的重任。随着新课标改革的落实,互联网技术的应用为职业学科教学带来了极大的便利,部分学校教务部门就网络教学和学习网站的建设进行了相关探索和研究。建设学习网站是对传统教学方式的改良与完善,在培养学生学科素养的同时,也便利了学生随时获取学习资源。文中对当下中职学校学习网站的教学设计及应用效果进行了分析,探究了中职学校学习网站的教学设计存在的问题,并提出了改进建议。

我国中小学电子学辅的供需分析及发展对策

为了弥补课堂教学不能有效兼顾学生个性化学习的不足,支持课外自主学习的电子学辅应运而生。本文采用调查研究、活动流程分析和对比分析的研究方法,分析电子学辅的内涵、比较电子学辅与电子教辅的区别,并对其分类;在分析学生课外学习活动的基础上,明确电子学辅的需求,并设计电子学辅的功能;剖析电子学辅的供给链,并通过对国内外典型电子学辅网站的资源和功能的调研,分析当前我国电子学辅供给的现状;针对国内电子学辅存在的内容覆盖面不全、资源兼容性较差、缺少个性化诊断支持、互动性不足、家长参与度不够、支持政策缺失等问题,从政策制定、资源内容、技术规范、学科交叉和使用机制五方面提出针对性的策略,以促进电子学辅行业的稳步、健康、有序发展。

网上拍卖中卖者声誉的非对称性研究

本文采用贝叶斯学习分析了网上拍卖中卖方声誉非对称现象产生的原因,并利用从淘宝网站收集的书画和书籍类物品的竞价数据,实证检验了卖方获得的好评次数与差评次数对拍卖物品成交概率和成交价格的影响。研究结果表明,买方对卖方的好评(差评)将增加(减少)新的买方对拍卖物品的预期价值,进而增加(减少)物品的成交概率和成交价格。此外,卖方所获差评的影响大于好评的影响,并且这种非对称性效应在容易辨别其质量的物品拍卖中更为明显。