Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint r...Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.展开更多
This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
This paper investigates whether security headers are enforced to mitigate cyber-attacks in web-based systems in cyberspace. The security headers examined include X-Content-Type-Options, X-Frame-Options, Strict-Transpo...This paper investigates whether security headers are enforced to mitigate cyber-attacks in web-based systems in cyberspace. The security headers examined include X-Content-Type-Options, X-Frame-Options, Strict-Transport-Security, Referrer-Policy, Content-Security-Policy, and Permissions-Policy. The study employed a controlled experiment using a security header analysis tool. The web-based applications (websites) were analyzed to determine whether security headers have been correctly implemented. The experiment was iterated for 100 universities in Africa which are ranked high. The purposive sampling technique was employed to understand the status quo of the security headers implementations. The results revealed that 70% of the web-based applications in Africa have not enforced security headers in web-based applications. The study proposes a secure system architecture design for addressing web-based applications’ misconfiguration and insecure design. It presents security techniques for securing web-based applications through hardening security headers using automated threat modelling techniques. Furthermore, it recommends adopting the security headers in web-based applications using the proposed secure system architecture design.展开更多
This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data ...This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.展开更多
Web 2.0的出现使信息构建(IA)的内容发生了深刻变化,IA已进入"信息构建2.0"(IA2.0)阶段。在IA2.0阶段,IA作为一门学科、一种角色和一类社团的协调统一体而存在,它强调真正"以用户为中心"和"丰富的用户体验&qu...Web 2.0的出现使信息构建(IA)的内容发生了深刻变化,IA已进入"信息构建2.0"(IA2.0)阶段。在IA2.0阶段,IA作为一门学科、一种角色和一类社团的协调统一体而存在,它强调真正"以用户为中心"和"丰富的用户体验"的核心理念,以满足新环境下的用户需求。Web2.0网站的IA,是IA2.0的典型应用,也是IA2.0阶段研究的主要内容,本文将其称为网站IA2.0。文中设计了一个网站IA2.0模型,并进行了简单的实例分析。展开更多
Web 2.0的出现使得"网络推手"现象已经成为目前网络中不可忽略的一个现象,并且随着众多网络推手公司等的成立,"网络推手"行业正逐渐产业化。采用案例研究方法,并经时序分析和逻辑模型分析步骤,通过对所选取的"...Web 2.0的出现使得"网络推手"现象已经成为目前网络中不可忽略的一个现象,并且随着众多网络推手公司等的成立,"网络推手"行业正逐渐产业化。采用案例研究方法,并经时序分析和逻辑模型分析步骤,通过对所选取的"天仙MM"和"我爸是李刚"事件的分析,研究得出"网络推手"事件的演进阶段;寻找出"网络推手"炒作的基本流程,并构建了"网络推手"炒作模式图。研究具有深入了解"网络推手"现象炒作原理和其中群体协作模式的意义。展开更多
基金supported in part by the National Science Foundation of China under Grants U22B2027,62172297,62102262,61902276 and 62272311,Tianjin Intelligent Manufacturing Special Fund Project under Grant 20211097the China Guangxi Science and Technology Plan Project(Guangxi Science and Technology Base and Talent Special Project)under Grant AD23026096(Application Number 2022AC20001)+1 种基金Hainan Provincial Natural Science Foundation of China under Grant 622RC616CCF-Nsfocus Kunpeng Fund Project under Grant CCF-NSFOCUS202207.
文摘Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
文摘This paper investigates whether security headers are enforced to mitigate cyber-attacks in web-based systems in cyberspace. The security headers examined include X-Content-Type-Options, X-Frame-Options, Strict-Transport-Security, Referrer-Policy, Content-Security-Policy, and Permissions-Policy. The study employed a controlled experiment using a security header analysis tool. The web-based applications (websites) were analyzed to determine whether security headers have been correctly implemented. The experiment was iterated for 100 universities in Africa which are ranked high. The purposive sampling technique was employed to understand the status quo of the security headers implementations. The results revealed that 70% of the web-based applications in Africa have not enforced security headers in web-based applications. The study proposes a secure system architecture design for addressing web-based applications’ misconfiguration and insecure design. It presents security techniques for securing web-based applications through hardening security headers using automated threat modelling techniques. Furthermore, it recommends adopting the security headers in web-based applications using the proposed secure system architecture design.
文摘This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.
