检索结果-南通市图书馆

A Study of Ensemble Feature Selection and Adversarial Training for malicious user detection

China Communications 2023年第10期20卷 212-229页

作者： Linjie Zhang Xiaoyan Zhu Jianfeng Ma State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China

The continuously booming of information technology has shed light on developing a variety of communication networks,multimedia,social networks and Internet of Things ***,users inevitably suffer from the intrusion of malicious *** studies focus on static characteristics of malicious users,which is easy to be bypassed by camouflaged malicious *** this paper,we present a malicious user detection method based on ensemble feature selection and adversarial ***,the feature selection alleviates the dimension disaster problem and achieves more accurate classification ***,we embed features into the multidimensional space and aggregate it into a feature map to encode the explicit content preference and implicit interaction ***,we use an effective ensemble learning which could avoid over-fitting and has good noise ***,we propose a datadriven neural network detection model with the regularization technique adversarial training to deeply analyze the *** simplifies the parameters,obtaining more robust interaction features and pattern *** demonstrate the effectiveness of our approach with numerical simulation results for malicious user detection,where the robustness issues are notable concerns.

关键词： malicious user detection feature selection ensemble learning adversarial training

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Modeling Multi-factor Sequential user Behavior Data over Social Networks

引用

Chinese Journal of Electronics 2016年第2期25卷 364-371页

作者： WANG Peng ZHANG Peng ZHOU Chuan GUO Li FANG Binxing YANG Tao Institute of Computing Technology Chinese Academy of Sciences Institute of Information Engineering Chinese Academy of Sciences Beijing University of Posts and Telecommunications China Information Technology Security Evaluation Center

Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time-varying user behavior is commonly influenced by multiple factors: user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesian model HES to model dynamic user behavior *** take the influential factors and user’s selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large-scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data.

关键词： malicious user detection user behavior Social networks Bayesian model Social influence

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：