拥有独立服务器的音乐网站,由于网络广告、版权等问题,增加了用户的开支成本。研究一种免费、方便使用的音乐分享网站显得十分必要。本文基于一种脚本语言(active server page.NET,ASP.NET)技术设计了在线音乐网站。首先分析了所用的关...拥有独立服务器的音乐网站,由于网络广告、版权等问题,增加了用户的开支成本。研究一种免费、方便使用的音乐分享网站显得十分必要。本文基于一种脚本语言(active server page.NET,ASP.NET)技术设计了在线音乐网站。首先分析了所用的关键技术和功能需求,其次完成了数据库的设计,最后实现了注册登录功能、界面设计、播放音乐、后台管理模块、测试模块等设计。经测试,该音乐分享网站设计基本达到了预期的目标。展开更多
This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection.The study selects a public network dataset containing 1781 data records,divides the dataset by 70...This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection.The study selects a public network dataset containing 1781 data records,divides the dataset by 70%for training and 30%for validation,and analyses the correlation between features using a correlation matrix.The experimental results show that the Elastic Net feature selection method generally outperforms PCA in all models,especially when combined with the Random Forest and XGBoost models,and the ElasticNet+Random Forest model achieves the highest accuracy of 0.968 and AUC value of 0.983,while the Kappa and MCC also reached 0.839 and 0.844 respectively,showing extremely high consistency and correlation.This indicates that combining Elastic Net feature selection and Random Forest model has significant performance advantages in online fraud detection.展开更多
Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial fea...Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.展开更多
文摘拥有独立服务器的音乐网站,由于网络广告、版权等问题,增加了用户的开支成本。研究一种免费、方便使用的音乐分享网站显得十分必要。本文基于一种脚本语言(active server page.NET,ASP.NET)技术设计了在线音乐网站。首先分析了所用的关键技术和功能需求,其次完成了数据库的设计,最后实现了注册登录功能、界面设计、播放音乐、后台管理模块、测试模块等设计。经测试,该音乐分享网站设计基本达到了预期的目标。
基金Guangdong Innovation and Entrepreneurship Training Programme for Undergraduates“Automatic Classification and Identification of Fraudulent Websites Based on Machine Learning”(Project No.:DC2023125)。
文摘This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection.The study selects a public network dataset containing 1781 data records,divides the dataset by 70%for training and 30%for validation,and analyses the correlation between features using a correlation matrix.The experimental results show that the Elastic Net feature selection method generally outperforms PCA in all models,especially when combined with the Random Forest and XGBoost models,and the ElasticNet+Random Forest model achieves the highest accuracy of 0.968 and AUC value of 0.983,while the Kappa and MCC also reached 0.839 and 0.844 respectively,showing extremely high consistency and correlation.This indicates that combining Elastic Net feature selection and Random Forest model has significant performance advantages in online fraud detection.
基金supported by the National Defense Basic Scientific Research Program of China(No.JCKY2023602C026)the funding of Key Laboratory of Mobile Application Innovation and Governance Technology,Ministry of Industry and Information Technology(2023IFS080601-K).
文摘Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.
