检索结果-南通市图书馆

Towards understanding bogus traffic service in online social networks

Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 415-431页

作者： Ping HE Xuhong ZHANG Changting LIN Ting WANG shouling ji College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Binjiang Institute of Zhejiang University Hangzhou 310027China College of Information Sciences and Technology Pennsylvania State UniversityUniversity Park 17057-4846USA

Critical functionality and huge infuence of the hot trend/topic page(HTP)in microblogging sites have driven the creation of a new kind of underground service called the bogus traffic service(BTS).BTS provides a kind of illegal service which hijacks the HTP by pushing the controlled topics into it for malicious customers with the goal of guiding public opinions.To hijack HTP,the agents of BTS maintain an army of black-market accounts called bogus trafic accounts(BTAs)and control BTAs to generate a burst of fake trafic by massively retweeting the tweets containing the customer desired topic(hashtag).Although this service has been extensively exploited by malicious customers,little has been done to understand it.In this paper,we conduct a systematic measurement study of the BTS.We first investigate and collect 125 BTS agents from a variety of sources and set up a honey pot account to capture BTAs from these agents.We then build a BTA detector that detects 162218 BTAs from Weibo,the largest Chinese microblogging site,with a precision of 94.5%.We further use them as a bridge to uncover 296916 topics that might be involved in bogus trafic.Finally,we uncover the operating mechanism from the perspectives of the attack cycle and the attack entity.The highlights of our findings include the temporal attack patterns and intelligent evasion tactics of the BTAs.These findings bring BTS into the spotlight.Our work will help in understanding and ultimately eliminating this threat.

关键词： Online social networks Measurement Bogus traffic Black market

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

Text-Based Price Recommendation System for Online Rental Houses

引用

Big Data Mining and Analytics 2020年第2期3卷 143-152页

作者： Lujia Shen Qianjun Liu Gong Chen shouling ji Department of Computer Science and Technology Zhejiang UniversityHangzhou 310027China School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30318USA

Online short-term rental platforms,such as Airbnb,have been becoming popular,and a better pricing strategy is imperative for hosts of new listings.In this paper,we analyzed the relationship between the description of each listing and its price,and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings.We used deep learning techniques(e.g.,feedforward network,long short-term memory,and mean shift)to design and implement TAPE.Using two chronologically extracted datasets of the same four cities,we revealed important factors(e.g.,indoor equipment and high-density area)that positively or negatively affect each property’s price,and evaluated our preliminary and enhanced models.Our models achieved a Root-Mean-Square Error(RMSE)of 33.73 in Boston,20.50 in London,34.68 in Los Angeles,and 26.31 in New York City,which are comparable to an existing model that uses more features.

关键词： price recommendation natural language processing sentence embedding Long Short-Term Memory(LSTM) mean shift

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

Spreading Social Influence with both Positive and Negative Opinions in Online Networks

引用

Big Data Mining and Analytics 2019年第2期2卷 100-117页

作者： jing (Selena) He Meng Han shouling ji Tianyu Du Zhao Li College of Computing and Software Engineering at Kennesaw State University Department of Computer Science at Zhejiang University Alibaba Group

Social networks are important media for spreading information, ideas, and influence among individuals.Most existing research focuses on understanding the characteristics of social networks, investigating how information is spread through the "word-of-mouth" effect of social networks, or exploring social influences among individuals and groups. However, most studies ignore negative influences among individuals and groups. Motivated by the goal of alleviating social problems, such as drinking, smoking, and gambling, and influence-spreading problems, such as promoting new products, we consider positive and negative influences, and propose a new optimization problem called the Minimum-sized Positive Influential Node Set(MPINS) selection problem to identify the minimum set of influential nodes such that every node in the network can be positively influenced by these selected nodes with no less than a threshold of ?. Our contributions are threefold. First, we prove that, under the independent cascade model considering positive and negative influences, MPINS is APX-hard. Subsequently, we present a greedy approximation algorithm to address the MPINS selection problem. Finally, to validate the proposed greedy algorithm, we conduct extensive simulations and experiments on random graphs and seven different realworld data sets that represent small-, medium-, and large-scale networks.

关键词： influence spread social networks positive influential node set greedy algorithm positive and negative influences

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

Towards Understanding the Security of Modern Image Captchas and Underground Captcha-Solving Services

引用

Big Data Mining and Analytics 2019年第2期2卷 118-144页

作者： Haiqin Weng Binbin Zhao shouling ji jianhai Chen Ting Wang Qinming He Raheem Beyah College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Lehigh University School of Electrical and Computer Engineering Georgia Institute of Technology

Image captchas have recently become very popular and are widely deployed across the Internet to defend against abusive programs. However, the ever-advancing capabilities of computer vision have gradually diminished the security of image captchas and made them vulnerable to attack. In this paper, we first classify the currently popular image captchas into three categories: selection-based captchas, slide-based captchas, and click-based captchas. Second, we propose simple yet powerful attack frameworks against each of these categories of image captchas. Third, we systematically evaluate our attack frameworks against 10 popular real-world image captchas,including captchas from ***, ***, and ***. Fourth, we compare our attacks against nine online image recognition services and against human labors from eight underground captcha-solving services. Our evaluation results show that(1) each of the popular image captchas that we study is vulnerable to our attacks;(2) our attacks yield the highest captcha-breaking success rate compared with state-of-the-art methods in almost all scenarios; and(3) our attacks achieve almost as high a success rate as human labor while being much faster.Based on our evaluation, we identify some design flaws in these popular schemes, along with some best practices and design principles for more secure captchas. We also examine the underground market for captcha-solving services, identifying 152 such services. We then seek to measure this underground market with data from these services. Our findings shed light on understanding the scale, impact, and commercial landscape of the underground market for captcha solving.

关键词： image captchas captcha security captcha-solving service underground market

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

Neural Network Model for Classifying the Economic Recession and Construction of Financial Stress Index

引用

国际计算机前沿大会会议论文集 2019年第2期 577-578页

作者： Lujia Shen Tianyu Du shouling ji Northeastern University Bsoton MA 02120 USA Zhejiang Univerisity Hangzhou Zhejiang China

In this paper, a C5.0 decision tree and neural network models are proposed to classify recessions in the US with 12 common financial indices and new financial stress indices inferred from the neural network models are created. A detailed experiment is presented and demonstrates that the neural network models with proper regularization and dropout achieve 98% accuracy in the training set, 97% accuracy in validation set and 100% accuracy in test accuracy. The financial stress indices outperform other existing financial stress indices in many scenes and can accurately locate crisis events even the most recent 2018 US Bear Market. With these models and new indices, contraction can be detected before NBER’s announcement and action could be taken as early as the situation get worse.

关键词： Financial crisis Neural network Financial stress index

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Reliable and Energy Efficient Target Coverage for Wireless Sensor Networks

引用

Tsinghua Science and Technology 2011年第5期16卷 464-474页

作者： jing He shouling ji Yi Pan Yingshu Li Department of Computer Science Georgia State University Atlanta GA 30303-3044 USA

A critical aspect of applications with Wireless Sensor Networks （WSNs） is network lifetime. Power-constrained WSNs are usable as long as they can communicate sense data to a processing node. Poor communication links and hazardous environments make the WSNs unreliable. Existing schemes assume that the state of a sensor covering targets is binary： success （covers the targets） or failure （cannot cover the targets）. However, in real WSNs, a sensor covers targets with a certain probability. To improve WSNs＇ reliability, we should consider that a sensor covers targets with users＇ satisfied probability. To solve this problem, this paper first introduces a failure probability into the target coverage problem to improve and control the system reliability. Furthermore, we model the solution as the a-Reliable Maximum Sensor Covers （a-RMSC） problem and design a heuristic greedy algorithm that efficiently computes the maximal number of a-Reliable sensor covers. To efficiently extend the WSNs lifetime with users＇ pre-defined failure probability requirements, only the sensors from the current active sensor cover are responsible for monitoring all targets, while all other sensors are in a low-energy sleep mode. Simulation results validate the performance of this algorithm, in which users can precisely control the system reliability without sacrificing much energy consumption.

关键词： target coverage wireless sensor networks energy efficiency sensor scheduling α-reliable maximum sensor covers node failure

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

多模态深度伪造及检测技术综述

引用

计算机研究与发展 2023年第6期60卷 1396-1416页

作者：李泽宇张旭鸿蒲誉文伍一鸣纪守领浙江大学计算机科学与技术学院杭州310007 浙江大学控制科学与工程学院杭州310007

随着各种深度学习生成模型在各领域的应用,生成的多媒体文件的真伪越来越难以辨别,深度伪造技术也因此得以诞生和发展.深度伪造技术通过深度学习相关技术能够篡改视频或者图片中的人脸身份信息、表情和肢体动作,以及生成特定人物的虚假... 详细信息

随着各种深度学习生成模型在各领域的应用,生成的多媒体文件的真伪越来越难以辨别,深度伪造技术也因此得以诞生和发展.深度伪造技术通过深度学习相关技术能够篡改视频或者图片中的人脸身份信息、表情和肢体动作,以及生成特定人物的虚假语音.自2018年Deepfakes技术在社交网络上掀起换脸热潮开始,大量的深度伪造方法被提出,并展现了其在教育、娱乐等领域的潜在应用.但同时深度伪造技术在社会舆论、司法刑侦等方面产生的负面影响也不容忽视.因此有越来越多的对抗手段被提出用于防止深度伪造被不法分子所应用,如深度伪造的检测和水印.首先,针对不同模态类型的深度伪造技术以及相应的检测技术进行了回顾和总结,并根据研究目的和研究方法对现有的研究进行了分析和归类;其次,总结了近年研究中广泛使用的视频和音频数据集;最后,探讨了该领域未来发展面临的机遇和挑战.

关键词：深度伪造深度伪造检测深度学习人脸替换生成对抗网络

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

基于层次结构的隐私多维分析查询算法

引用

中国科学：信息科学 2023年第6期53卷 1111-1131页

作者：张啸剑周丹徐雅鑫林东岱纪守领孟小峰河南财经政法大学计算机与信息工程学院郑州450002 中国科学院信息工程研究所北京100093 浙江大学计算机科学与技术学院杭州310027 中国人民大学信息学院北京100872

基于本地化差分隐私的多维分析查询(multi-dimensional analytical query,MDA)已得到了研究者的广泛关注.现有基于最优局部哈希(optimal local Hashing,OLH)机制与层次树结构的扰动方法存在泄露根结点隐私的风险.针对现有结合层次树结... 详细信息

基于本地化差分隐私的多维分析查询(multi-dimensional analytical query,MDA)已得到了研究者的广泛关注.现有基于最优局部哈希(optimal local Hashing,OLH)机制与层次树结构的扰动方法存在泄露根结点隐私的风险.针对现有结合层次树结构的本地扰动机制不足,提出了一种有效且满足本地化差分隐私的MDA查询算法H4MDA (hierarchical structure for MDA),该算法充分利用层次树的横向与纵向结构特征设计了3种基于用户分组策略的本地扰动算法HGRR,LGRR-FD,LGRR.算法HGRR结合层次树横向结构与GRR机制本地扰动用户元组数据,通过摈弃根结点组合来响应MDA查询.不同于HGRR,LGRR-FD算法利用层次树的纵向结构与GRR机制扰动本地数据,同时通过添加假数据来避免叶子结点的隐私泄露.LGRR算法通过摈弃叶子结点层纵向扰动本地数据.收集者结合LGRR的扰动结果利用局部一致性处理技术重构层次树最后两层,通过添加虚拟叶子结点来响应MDA查询,而虚拟叶子结点计数之和等于其父节点计数.HGRR,LGRR-FD,LGRR算法与现有扰动算法在3种数据集上实验结果表明,其响应MDA查询的精度优于同类算法.

关键词：多维分析查询层次结构本地化差分隐私本地扰动随机应答机制

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

机器学习模型可解释性方法、应用与安全研究综述

引用

计算机研究与发展 2019年第10期56卷 2071-2096页

作者：纪守领李进锋杜天宇李博浙江大学计算机科学与技术学院网络空间安全研究中心杭州310027 伊利诺伊大学香槟分校计算机科学学院美国伊利诸伊州厄巴纳香槟61822

尽管机器学习在许多领域取得了巨大的成功,但缺乏可解释性严重限制了其在现实任务尤其是安全敏感任务中的广泛应用.为了克服这一弱点,许多学者对如何提高机器学习模型可解释性进行了深入的研究,并提出了大量的解释方法以帮助用户理解模... 详细信息

尽管机器学习在许多领域取得了巨大的成功,但缺乏可解释性严重限制了其在现实任务尤其是安全敏感任务中的广泛应用.为了克服这一弱点,许多学者对如何提高机器学习模型可解释性进行了深入的研究,并提出了大量的解释方法以帮助用户理解模型内部的工作机制.然而,可解释性研究还处于初级阶段,依然还有大量的科学问题尚待解决.并且,不同的学者解决问题的角度不同,对可解释性赋予的含义也不同,所提出的解释方法也各有侧重.迄今为止,学术界对模型可解释性仍缺乏统一的认识,可解释性研究的体系结构尚不明确.在综述中,回顾了机器学习中的可解释性问题,并对现有的研究工作进行了系统的总结和科学的归类.同时,讨论了可解释性相关技术的潜在应用,分析了可解释性与可解释机器学习的安全性之间的关系,并且探讨了可解释性研究当前面临的挑战和未来潜在的研究方向,以期进一步推动可解释性研究的发展和应用.

关键词：机器学习可解释性解释方法可解释机器学习安全性

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

一种面向图神经网络的图重构防御方法

引用

计算机研究与发展 2021年第5期58卷 1075-1091页

作者：陈晋音黄国瀚张敦杰张旭鸿纪守领浙江工业大学网络空间安全研究院杭州310023 浙江工业大学信息工程学院杭州310023 浙江大学控制科学与工程学院杭州310007 浙江大学计算机科学与技术学院杭州310007

近年来,图神经网络在图表示学习领域中取得了较好表现广泛应用于日常生活中,例如电子商务、社交媒体和生物学等.但是研究表明,图神经网络容易受到精心设计的对抗攻击迷惑,使其无法正常工作.因此,提高图神经网络的鲁棒性至关重要.已有研... 详细信息

近年来,图神经网络在图表示学习领域中取得了较好表现广泛应用于日常生活中,例如电子商务、社交媒体和生物学等.但是研究表明,图神经网络容易受到精心设计的对抗攻击迷惑,使其无法正常工作.因此,提高图神经网络的鲁棒性至关重要.已有研究提出了一些提高图神经网络鲁棒性的防御方法,然而如何在确保模型主任务性能的前提下降低对抗攻击的攻击成功率仍存在挑战.通过观察不同攻击产生的对抗样本发现,对抗攻击生成的对抗连边所对应的节点对之间通常存在低结构相似性和低节点特征相似性的特点.基于上述发现,提出了一种面向图神经网络的图重构防御方法GRD-GNN,分别从图结构和节点特征考虑,采用共同邻居数和节点相似度2种相似度指标检测对抗连边并实现图重构,使得重构的图结构删除对抗连边,且添加了增强图结构关键特征的连边,从而实现有效防御.最后,论文在3个真实数据集上展开防御实验,验证了GRD-GNN相比其他防御方法均能取得最佳的防御性能,且不影响正常图数据的分类任务.此外,利用可视化方法对防御结果做解释,解析方法的有效性.

关键词：图重构对抗攻击图神经网络图表示学习节点分类

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

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：