检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

54 篇 会议

馆藏范围

54 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

50 篇 工学
- 49 篇 计算机科学与技术...
- 24 篇 信息与通信工程
- 10 篇 软件工程
- 3 篇 电气工程
- 2 篇 控制科学与工程
- 2 篇 化学工程与技术
- 2 篇 生物医学工程（可授...
- 2 篇 生物工程
- 1 篇 仪器科学与技术
11 篇 理学
- 9 篇 数学
- 2 篇 化学
- 2 篇 生物学
2 篇 医学
- 2 篇 基础医学(可授医学...
- 2 篇 临床医学
- 2 篇 公共卫生与预防医...
2 篇 管理学
- 1 篇 管理科学与工程(可...
- 1 篇 图书情报与档案管...
1 篇 法学
- 1 篇 法学

主题

11 篇 privacy
5 篇 security
4 篇 internet of thin...
4 篇 blockchain
3 篇 intel sgx
3 篇 iot
3 篇 differential pri...
3 篇 machine learning
2 篇 data confidentia...
2 篇 data privacy
2 篇 user privacy
2 篇 attacks
2 篇 vulnerability
2 篇 trust management
2 篇 federated learni...
1 篇 cloud computing:...
1 篇 cyber attacks
1 篇 ethereum
1 篇 threats
1 篇 smart homes

机构

2 篇 univ coimbra cis...
2 篇 univ neuchatel n...
1 篇 north carolina s...
1 篇 state grid jiang...
1 篇 montclair state ...
1 篇 promotion founda...
1 篇 univ porto cisuc...
1 篇 stevens inst tec...
1 篇 university of ma...
1 篇 colroado sch min...
1 篇 university of te...
1 篇 univ fed campina...
1 篇 inesc tec porto
1 篇 nashua high scho...
1 篇 iit roorkee utta...
1 篇 north carolina s...
1 篇 univ houston hou...
1 篇 nashua high scho...
1 篇 indian inst tech...
1 篇 university of lu...

作者

2 篇 cunha mariana
2 篇 felber pascal
2 篇 vilela joao p.
2 篇 mendes ricardo
2 篇 huang xiuli
2 篇 sural shamik
1 篇 franzen fabian
1 篇 debasish jena
1 篇 le traon yves
1 篇 satvat kiavash
1 篇 wang liming
1 篇 duarte guilherme
1 篇 gupta peeyush
1 篇 huh young
1 篇 brito andrey
1 篇 rani p. jhansi
1 篇 zhang tong
1 篇 murynets ilona
1 篇 tsankov petar
1 篇 kalkan kubra

语言

53 篇 英文
1 篇 德文

检索条件"任意字段=10th ACM Conference on Data and Application Security and Privacy, CODASPY 2020"

共 54 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

IoTFuzzSentry: Hunting Bugs In the IoT Wilderness In Operational Phase Using Payload Fuzzing 24

IoTFuzzSentry: Hunting Bugs In The IoT Wilderness In Operati...

引用

14th acm conference on data and application security and privacy (acm codaspy)

作者： Chaudhary, Priyanka Rushikesh Krishna, N. Pranav Maiti, Rajib Ranjan BITS Pilani Hyderabad Campus Secunderabad India

ISBN: (纸本)9798400704215

In the operational phase, an IoT device runs a light-weight server that is responsible for responding to the user queries, like accessing video and taking a snap in a IoT camera. the flaws in the implementation of certain security mechanisms in these IoT devices can lead to varying level of security threats. In this paper, we address the problems of discovering such vulnerabilities using effective fuzzing via crafted transport and upper layer protocol packets in IoT communication. We have designed and developed a mutation-based fuzzing tool, named IoTFuzzSentry, to discover certain non-trivial vulnerabilities in commercial IoT devices and demonstrate their potential exploits. We have used IoT devices, like IP cameras and Smart Plug. We have categorized the vulnerabilities into four types (IoT Credential Leakage (V1), Sneak IP camera live video stream (V2), Creep Live IoT Image (V3), IoT Command Injection (V4)) and show their exploits using three IoT devices. We have reported all these vulnerabilities to respective vendors via email. We believe that our IoTFuzzSentry has a great potential to discover such unconventional security threats and allow IoT vendors to strengthen the security of their commercialized devices.

关键词： Mutation Fuzzing IoT Devices Vulnerability privacy

来源：评论

学校读者我要写书评

暂无评论

Privkit: A Toolkit of privacy-Preserving Mechanisms for Heterogeneous data Types 24

Privkit: A Toolkit of Privacy-Preserving Mechanisms for Hete...

引用

14th acm conference on data and application security and privacy (acm codaspy)

作者： Cunha, Mariana Duarte, Guilherme Andrade, Ricardo Mendes, Ricardo Vilela, Joao P. Univ Porto CISUC CRACS INESC TEC Porto Portugal Univ Porto Dept Comp Sci Porto Portugal Univ Coimbra CISUC Coimbra Portugal Univ Coimbra Dept Informat Engn Coimbra Portugal

ISBN: (纸本)9798400704215

With the massive data collection from different devices, spanning from mobile devices to all sorts of IoT devices, protecting the privacy of users is a fundamental concern. In order to prevent unwanted disclosures, several privacy-Preserving Mechanisms (PPMs) have been proposed. Nevertheless, due to the lack of a standardized and universal privacy definition, configuring and evaluating PPMs is quite challenging, requiring knowledge that the average user does not have. In this paper, we propose a privacy toolkit - Privkit - to systematize this process and facilitate automated configuration of PPMs. Privkit enables the assessment of privacy-preserving mechanisms with different configurations, while allowing the quantification of the achieved privacy and utility level of various types of data. Privkit is open source and can be extended with new data types, corresponding PPMs, as well as privacy and utility assessment metrics and privacy attacks over such data. this toolkit is available through a Python Package with several state-of-the-art PPMs already implemented, and also accessible through a Web application. Privkit constitutes a unified toolkit that makes the dissemination of new privacy-preserving methods easier and also facilitates reproducibility of research results, through a repository of Jupyter Notebooks that enable reproduction of research results.

关键词： privacy privacy Tools privacy-Preserving Mechanisms data privacy Python

来源：评论

学校读者我要写书评

暂无评论

Towards Accurate and Stronger Local Differential privacy for Federated Learning with Staircase Randomized Response 24

Towards Accurate and Stronger Local Differential Privacy for...

引用

14th acm conference on data and application security and privacy (acm codaspy)

作者： Varun, Matta Feng, Shuya Wang, Han Sural, Shamik Hong, Yuan Indian Inst Technol Kharagpur India Univ Connecticut Storrs CT USA Univ Kansas Lawrence KS USA

ISBN: (纸本)9798400704215

Federated Learning (FL), a privacy-preserving training approach, has proven to be effective, yet its vulnerability to attacks that extract information from model weights is widely recognized. To address such privacy concerns, Local Differential privacy (LDP) has been applied to FL: perturbing the weights trained for the local model by each client. However, besides high utility loss on the randomized model weights, we identify a new inference attack to the existing LDP method, that can reconstruct the original value from the noisy values with high confidence. To mitigate these issues, in this paper, we propose the Staircase Randomized Response (SRR)-FL framework, which assigns higher probabilities to weights closer to the true weight, reducing the distance between the true and perturbed data. this minimizes the noise for maintaining the same LDP guarantee, leading to better utility. Compared to existing LDP mechanisms (e.g., Generalized Randomized Response) on the FL, SRR-FL can further provide a more accurate privacy-preserving training model, and enhance the robustness against the inference attack while ensuring the same LDP guarantee. Furthermore, we also use the parameter shuffling method for privacy amplification. the efficacy of SRR-FL has been validated on widely used datasets MNIST, Medical-MNIST and CIFAR-10, demonstrating remarkable performance.

关键词： Local differential privacy federated learning client-level LDP

来源：评论

学校读者我要写书评

暂无评论

Infotainment System Matters: Understanding the Impact and Implications of In-Vehicle Infotainment System Hacking with Automotive Grade Linux 23

Infotainment System Matters: Understanding the Impact and Im...

引用

13th acm conference on data and application security and privacy (codaspy)

作者： Jeong, Seonghoon Ryu, Minsoo Kang, Hyunjae Kim, Huy Kang Korea Univ Sch Cybersecur Seoul South Korea

ISBN: (纸本)9798400700675

An in-vehicle infotainment (IVI) system is connected to heterogeneous networks such as Controller Area Network bus, Bluetooth, Wi-Fi, cellular, and other vehicle-to-everything communications. An IVI system has control of a connected vehicle and deals with privacy-sensitive information like current geolocation and destination, phonebook, SMS, and driver's voice. Several offensive studies have been conducted on IVI systems of commercialized vehicles to show the feasibility of car hacking. However, to date, there has been no comprehensive analysis of the impact and implications of IVI system exploitations. To understand security and privacy concerns, we provide our experience hosting an IVI system hacking competition, Cyber security Challenge 2021 (CSC2021). We use a feature-flavored infotainment operating system, Automotive Grade Linux (AGL). the participants gathered and submitted 33 reproducible and verified proofs-of-concept exploit codes targeting 11 components of the AGL-based IVI testbed. the participants exploited four vulnerabilities to steal various data, manipulate the IVI system, and cause a denial of service. the data leakage includes privacy, personally identifiable information, and cabin voice. the participants proved lateral movement to electronic control units and smartphones. We conclude with lessons learned with three mitigation strategies to enhance the security of the IVI system.

关键词： car hacking cybersecurity competition vulnerability exploit Automotive Grade Linux privacy leakage

来源：评论

学校读者我要写书评

暂无评论

Precision Guided Approach to Mitigate data Poisoning Attacks in Federated Learning 24

Precision Guided Approach to Mitigate Data Poisoning Attacks...

引用

14th acm conference on data and application security and privacy (acm codaspy)

作者： Kumar, K. Naveen Mohan, C. Krishna Machiry, Aravind Indian Inst Technol Comp Sci & Engn Hyderabad India Purdue Univ Elect & Comp Engn Wesy Lafayatte IN USA

ISBN: (纸本)9798400704215

Federated Learning (FL) is a collaborative learning paradigm enabling participants to collectively train a shared machine learning model while preserving the privacy of their sensitive data. Nevertheless, the inherent decentralized and data-opaque characteristics of FL render its susceptibility to data poisoning attacks. these attacks introduce malformed or malicious inputs during local model training, subsequently influencing the global model and resulting in erroneous predictions. Current FL defense strategies against data poisoning attacks either involve a trade-off between accuracy and robustness or necessitate the presence of a uniformly distributed root dataset at the server. To overcome these limitations, we present FedZZ, which harnesses a zone-based deviating update (ZBDU) mechanism to effectively counter data poisoning attacks in FL. the ZBDU approach identifies the clusters of benign clients whose collective updates exhibit notable deviations from those of malicious clients engaged in data poisoning attack. Further, we introduce a precision-guided methodology that actively characterizes these client clusters (zones), which in turn aids in recognizing and discarding malicious updates at the server. Our evaluation of FedZZ across two widely recognized datasets: CIFAR10 and EMNIST, demonstrate its efficacy in mitigating data poisoning attacks, surpassing the performance of prevailing state-of-the-art methodologies in both single and multi-client attack scenarios and varying attack volumes. Notably, FedZZ also functions as a robust client selection strategy, even in highly non-IID and attack-free scenarios. Moreover, in the face of escalating poisoning rates, the model accuracy attained by FedZZ displays superior resilience compared to existing techniques. For instance, when confronted with a 50% presence of malicious clients, FedZZ sustains an accuracy of 67.43%, while the accuracy of the second-best solution, FL-Defender, diminishes to 43.36%.

关键词： Federated learning data poisonous attacks defense precision guided

来源：评论

学校读者我要写书评

暂无评论

Velocity-Aware Geo-Indistinguishability 23

Velocity-Aware Geo-Indistinguishability

引用

13th acm conference on data and application security and privacy (codaspy)

作者： Mendes, Ricardo Cunha, Mariana Vilela, Joao P. Univ Coimbra CISUC Coimbra Portugal Univ Coimbra Dept Informat Coimbra Portugal Univ Porto Fac Ciencias CISUC CRACS INESCTEC Rua Campo Alegre S-N P-4169007 Porto Portugal Univ Porto Fac Ciencias Dept Ciencia Comp Rua Campo Alegre S-N P-4169007 Porto Portugal

ISBN: (纸本)9798400700675

Location privacy-Preserving Mechanisms (LPPMs) have been proposed to mitigate the risks of privacy disclosure yielded from location sharing. However, due to the nature of this type of data, spatio-temporal correlations can be leveraged by an adversary to extenuate the protections. Moreover, the application of LPPMs at collection time has been limited due to the difficulty in configuring the parameters and in understanding their impact on the privacy level by the end-user. In this work we adopt the velocity of the user and the frequency of reports as a metric for the correlation between location reports. Based on such metric we propose a generalization of Geo-Indistinguishability denoted Velocity-Aware Geo-Indistinguishability (VA-GI). We define a VA-GI LPPM that provides an automatic and dynamic trade-off between privacy and utility according to the velocity of the user and the frequency of reports. this adaptability can be tuned for general use, by using city or country-wide data, or for specific user profiles, thus warranting fine-grained tuning for users or environments. Our results using vehicular trajectory data show that VA-GI achieves a dynamic trade-off between privacy and utility that outperforms previous works. Additionally, by using a Gaussian distribution as estimation for the distribution of the velocities, we provide a methodology for configuring our proposed LPPM without the need for mobility data. this approach provides the required privacy-utility adaptability while also simplifying its configuration and general application in different contexts.

关键词： Location privacy Differential privacy Adaptive privacy privacy Budget Location-Based Services

来源：评论

学校读者我要写书评

暂无评论

Tool/dataset Paper: Realistic ABAC data Generation using Conditional Tabular GAN 23

Tool/Dataset Paper: Realistic ABAC Data Generation using Con...

引用

13th acm conference on data and application security and privacy (codaspy)

作者： Rai, Ritwik Sural, Shamik Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400700675

Attribute-based Access Control (ABAC) is increasingly being used in a wide variety of applications that include cloud services, IoT, smart homes, healthcare and several others. Conducting systematic and reproducible experiments with benchmark realistic datasets, however, still remains a challenge. To address this shortcoming, in this paper we introduce a method called ConGRASS (Conditional Tabular GAN for Realistic ABAC Simulation Studies) for generating large ABAC datasets. Starting with a given real world dataset of (potentially) limited size, we first train a conditional tabular generative adversarial network for learning its distribution. the trained model is used to generate realistic datasets of arbitrarily large sizes having distribution similar to the original dataset. ConGRASS has been implemented as a free to use web-based tool in which a user can choose the name of a listed real dataset along with the desired dataset size. A CSV file containing ABAC data is generated as output. Extensive evaluation shows the ability of the model to faithfully learn the statistical properties of the selected real data. When such a dataset is used in an actual problem, significant improvement in performance is achieved, proving the utility of ConGRASS.

关键词： Attribute-based Access Control (ABAC) Conditional GAN Tabular data generation ABAC simulation studies

来源：评论

学校读者我要写书评

暂无评论

Comparative privacy Analysis of Mobile Browsers 23

Comparative Privacy Analysis of Mobile Browsers

引用

13th acm conference on data and application security and privacy (codaspy)

作者： Zafar, Ahsan Das, Anupam North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9798400700675

Online trackers are invasive as they track our digital footprints, many of which are sensitive in nature, and when aggregated over time, they can help infer intricate details about our lifestyles and habits. Although much research has been conducted to understand the effectiveness of existing countermeasures for the desktop platform, little is known about how mobile browsers have evolved to handle online trackers. With mobile devices now generating more web traffic than their desktop counterparts, we fill this research gap through a large-scale comparative analysis of mobile web browsers. We crawl 10K valid websites from the Tranco list on real mobile devices. Our data collection process covers both popular generic browsers (e.g., Chrome, Firefox, and Safari) as well as privacy-focused browsers (e.g., Brave, Duck Duck Go, and Firefox-Focus). We use dynamic analysis of runtime execution traces and static analysis of source codes to highlight the tracking behavior of invasive fingerprinters. We also find evidence of tailored content being served to different browsers. In particular, we note that Firefox Focus sees altered script code, whereas Brave and Duck Duck Go have highly similar content. To test the privacy protection of browsers, we measure the responses of each browser in blocking trackers and advertisers and note the strengths and weaknesses of privacy browsers. To establish ground truth, we use well-known block lists, including EasyList, Easyprivacy, Disconnect and WhoTracksMe and find that Brave generally blocks the highest number of content that should be blocked as per these lists. Focus performs better against social trackers, and Duck Duck Go restricts third-party trackers that perform email-based tracking.

关键词： Web tracking User privacy Mobile browsers

来源：评论

学校读者我要写书评

暂无评论

security threats from Bitcoin Wallet Smartphone applications: Vulnerabilities, Attacks, and Countermeasures 21

Security Threats from Bitcoin Wallet Smartphone Applications...

引用

11th acm conference on data and application security and privacy (codaspy)

作者： Hu, Yiwen Wang, Sihan Tu, Guan-Hua Xiao, Li Xie, Tian Lei, Xinyu Li, Chi-Yu Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA Natl Chiao Tung Univ Dept Comp Sci Hsinchu Taiwan

ISBN: (纸本)9781450381437

Nowadays, Bitcoin is the most popular cryptocurrency. With the proliferation of smartphones and the high-speed mobile Internet, more and more users have started accessing their Bitcoin wallets on their smartphones. Users can download and install a variety of Bitcoin wallet applications (e.g., Coinbase, Luno, Bitcoin Wallet) on their smartphones and access their Bitcoin wallets anytime and anywhere. However, it is still unknown whether these Bitcoin wallet smartphone applications are secure or if they are new attack surfaces for adversaries to attack these application users. In this work, we explored the insecurity of the 10 most popular Bitcoin wallet smartphone applications and discovered three security vulnerabilities. By exploiting them, adversaries can launch various attacks including Bitcoin deanonymization, reflection and amplification spamming, and wallet fraud attacks. To address the identified security vulnerabilities, we developed a phone-side Bitcoin security Rectifier to secure Bitcoin wallet smartphone application users. the developed rectifier does not require any modifications to current wallet applications and is compliant with Bitcoin standards.

关键词： Bitcoin wallets Blockchain security Mobile networks

来源：评论

学校读者我要写书评

暂无评论

Developing A Compelling Vision for Winning the Cybersecurity Arms Race 20

Developing A Compelling Vision for Winning the Cybersecurity...

引用

10th acm conference on data and application security and privacy (codaspy)

作者： Bertino, Elisa Singhal, Anoop Srinivasagopalan, Srivathsan Verma, Rakesh Purdue Univ W Lafayette IN 47907 USA NIST Gaithersburg MD 20899 USA AT&T Cybersecur Austin TX USA Univ Houston Houston TX USA

ISBN: (纸本)9781450371070

In cybersecurity there is a continuous arms race between the attackers and the defenders. In this panel, we investigate three key questions regarding this arms race. First question is whether this arms race is winnable. Second, if the answer to the first question is in the affirmative, what steps we need to take to win this race. third, if the answer to the first question is negative, what is the justification for this and what steps can we take to improve the state of affairs and increase the bar for the attackers significantly.

关键词： Attacker attacks threats security advanced persistent threat data integrity

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共6页 << < 1 2 3 4 5 6 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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

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：