Journal of Cybersecurity and Privacy最新文献_第5页

Water-Tight IoT–Just Add Security 无懈可击的物联网——只需增加安全性

Journal of Cybersecurity and Privacy Pub Date : 2023-03-01 DOI: 10.3390/jcp3010006

G. Bour, C. Bosco, R. Ugarelli, M. Jaatun

引用次数: 2

The Cybersecurity Awareness INventory (CAIN): Early Phases of Development of a Tool for Assessing Cybersecurity Knowledge Based on the ISO/IEC 27032 网络安全意识清单(CAIN):基于ISO/IEC 27032的网络安全知识评估工具开发的早期阶段

Journal of Cybersecurity and Privacy Pub Date : 2023-02-16 DOI: 10.3390/jcp3010005

Giorgia Tempestini, E. Rovira, Aryn A. Pyke, Francesco Di Nocera

{"title":"The Cybersecurity Awareness INventory (CAIN): Early Phases of Development of a Tool for Assessing Cybersecurity Knowledge Based on the ISO/IEC 27032","authors":"Giorgia Tempestini, E. Rovira, Aryn A. Pyke, Francesco Di Nocera","doi":"10.3390/jcp3010005","DOIUrl":"https://doi.org/10.3390/jcp3010005","url":null,"abstract":"Knowledge of possible cyber threats as well as awareness of appropriate security measures plays a crucial role in the ability of individuals to not only discriminate between an innocuous versus a dangerous cyber event, but more importantly to initiate appropriate cybersecurity behaviors. The purpose of this study was to construct a Cybersecurity Awareness INventory (CAIN) to be used as an instrument to assess users’ cybersecurity knowledge by providing a proficiency score that could be correlated with cyber security behaviors. A scale consisting of 46 items was derived from ISO/IEC 27032. The questionnaire was administered to a sample of college students (N = 277). Based on cybersecurity behaviors reported to the research team by the college’s IT department, each participant was divided into three groups according to the risk reports they received in the past nine months (no risk, low risk, and medium risk). The ANOVA results showed a statistically significant difference in CAIN scores between those in the no risk and medium-risk groups; as expected, CAIN scores were lower in the medium-risk group. The CAIN has the potential to be a useful assessment tool for cyber training programs as well as future studies investigating individuals’ vulnerability to cyberthreats.","PeriodicalId":316237,"journal":{"name":"Journal of Cybersecurity and Privacy","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114679671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing 同态加密在制造业云计算用例中的潜力

Journal of Cybersecurity and Privacy Pub Date : 2023-02-06 DOI: 10.3390/jcp3010004

Raphael Kiesel, Marvin Lakatsch, Alexander Mann, Karl Lossie, F. Sohnius, R. Schmitt

{"title":"Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing","authors":"Raphael Kiesel, Marvin Lakatsch, Alexander Mann, Karl Lossie, F. Sohnius, R. Schmitt","doi":"10.3390/jcp3010004","DOIUrl":"https://doi.org/10.3390/jcp3010004","url":null,"abstract":"Homomorphic encryption enables secure cloud computing over the complete data lifecycle. As so-called in-use encryption methodology, it allows using encrypted data for, e.g., data analysis—in contrast to classic encryption methods. In-use encryption enables new ways of value creation and an extensive use of cloud computing for manufacturing companies. However, homomorphic encryption is not widely implemented in practice yet. This is mainly since homomorphic encryption has higher computation times and is limited regarding its calculation operations. Nevertheless, for some use cases, the security requirements are a lot stricter than, e.g., timeliness requirements. Thus, homomorphic encryption might be beneficial. This paper, therefore, analyzes the potential of homomorphic encryption for cloud computing in manufacturing. First, the potential and limitations for both classic and homomorphic encryption are presented on the basis of a literature review. Second, to validate the limitations, simulations are executed, comparing the computation time and data transfer of classic and homomorphic encryption. The results show that homomorphic encryption is a tradeoff of security, time, and cost, which highly depends on the use case. Therefore, third, manufacturing use cases are identified; the two use cases of predictive maintenance and contract manufacturing are presented in detail, demonstrating how homomorphic encryption can be beneficial.","PeriodicalId":316237,"journal":{"name":"Journal of Cybersecurity and Privacy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130829535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation 隐私标志观察站:实时隐私威胁评估的众包工具

Journal of Cybersecurity and Privacy Pub Date : 2023-01-29 DOI: 10.3390/jcp3010003

V. Vlachos, Y. Stamatiou, Sotiris Nikoletseas

{"title":"The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation","authors":"V. Vlachos, Y. Stamatiou, Sotiris Nikoletseas","doi":"10.3390/jcp3010003","DOIUrl":"https://doi.org/10.3390/jcp3010003","url":null,"abstract":"Instilling good privacy practices to developers and users appears to be a difficult and daunting task. The World Wide Web encompasses a panspermia of different technologies, commercial and open source apis, evolving security standards and protocols that can be deployed towards the implementation of complex, powerful, web applications. At the same time, the proliferation of applications and services on all types of devices has also increased the attack surface for privacy threats. In this paper, we present the Privacy Flag Observatory, a platform which is one of the main tools produced by the Privacy Flag eu funded research project. The goal of this initiative is to raise awareness among European citizens of the potential privacy threats that beset the software and services they trust and use every day, including websites and smartphone applications. The Privacy Flag Observatory is one of the components that contributed to a large extent, to the success of the project’s goals. It is a real-time security and privacy threat monitoring platform whose aim is to collect, archive, analyze and present security and privacy-related information to the broader public as well as experts. Although the platform relies on crowdsourcing information gathering strategies and interacts with several other components installed on users’ devices or remote servers and databases, in this paper, we focus on the observatory platform referring only cursorily to other components such as the mobile phone add-on.","PeriodicalId":316237,"journal":{"name":"Journal of Cybersecurity and Privacy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132325654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Acknowledgment to the Reviewers of Journal of Cybersecurity and Privacy in 2022 感谢《网络安全与隐私杂志》2022年审稿人

Journal of Cybersecurity and Privacy Pub Date : 2023-01-13 DOI: 10.3390/jcp3010002

Journal of Cybersecurity and Privacy Editorial Off

引用次数: 0

An Investigation to Detect Banking Malware Network Communication Traffic Using Machine Learning Techniques 利用机器学习技术检测银行恶意软件网络通信流量的研究

Journal of Cybersecurity and Privacy Pub Date : 2022-12-27 DOI: 10.3390/jcp3010001

M. Kazi, Steve Woodhead, D. Gan

{"title":"An Investigation to Detect Banking Malware Network Communication Traffic Using Machine Learning Techniques","authors":"M. Kazi, Steve Woodhead, D. Gan","doi":"10.3390/jcp3010001","DOIUrl":"https://doi.org/10.3390/jcp3010001","url":null,"abstract":"Banking malware are malicious programs that attempt to steal confidential information, such as banking authentication credentials, from users. Zeus is one of the most widespread banking malware variants ever discovered. Since the Zeus source code was leaked, many other variants of Zeus have emerged, and tools such as anti-malware programs exist that can detect Zeus; however, these have limitations. Anti-malware programs need to be regularly updated to recognise Zeus, and the signatures or patterns can only be made available when the malware has been seen. This limits the capability of these anti-malware products because they are unable to detect unseen malware variants, and furthermore, malicious users are developing malware that seeks to evade signature-based anti-malware programs. In this paper, a methodology is proposed for detecting Zeus malware network traffic flows by using machine learning (ML) binary classification algorithms. This research explores and compares several ML algorithms to determine the algorithm best suited for this problem and then uses these algorithms to conduct further experiments to determine the minimum number of features that could be used for detecting the Zeus malware. This research also explores the suitability of these features when used to detect both older and newer versions of Zeus as well as when used to detect additional variants of the Zeus malware. This will help researchers understand which network flow features could be used for detecting Zeus and whether these features will work across multiple versions and variants of the Zeus malware.","PeriodicalId":316237,"journal":{"name":"Journal of Cybersecurity and Privacy","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125074469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

User Reputation on E-Commerce: Blockchain-Based Approaches 电子商务中的用户声誉:基于区块链的方法

Journal of Cybersecurity and Privacy Pub Date : 2022-12-19 DOI: 10.3390/jcp2040046

Maria José Angélico Gonçalves, Rui Humberto Pereira, Marta Alexandra Guerra Magalhães Coelho

{"title":"User Reputation on E-Commerce: Blockchain-Based Approaches","authors":"Maria José Angélico Gonçalves, Rui Humberto Pereira, Marta Alexandra Guerra Magalhães Coelho","doi":"10.3390/jcp2040046","DOIUrl":"https://doi.org/10.3390/jcp2040046","url":null,"abstract":"User trust is a fundamental issue in e-commerce. To address this problem, recommendation systems have been widely used in different application domains including social media healthcare, e-commerce, and others. In this paper, we present a systematic review of the literature in the area of blockchain-based reputation models and we discuss the obtained results, answering the initial research questions. These findings lead us to conclude that the existing systems are based on a trusted third party (TTP) to collect and store reputation data, which does not provide transparency on users’ reputation scores. In the recent literature, on the one hand, blockchain-based reputation systems have been highlighted as possible solutions to effectively provide the necessary transparency, as well as effective identity management. On the other hand, new challenges are posed in terms of user privacy and performance, due to the specific characteristics of the blockchain. According to the literature, two major approaches have been proposed based on public and permissioned blockchains. Each approach applies adjusted models for calculating reputation scores. Despite the undoubted advantages added by a blockchain, the problem is only partially solved since there is no effective way to prevent blockchain oracles from feeding the chain with false, unfair, or biased data. In our future work, we intend to explore the two approaches discussed in the literature in order to propose a new blockchain-based model for deriving user reputation scores.","PeriodicalId":316237,"journal":{"name":"Journal of Cybersecurity and Privacy","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130607881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An Understanding of the Vulnerability of Datasets to Disparate Membership Inference Attacks 数据集易受不同隶属关系推理攻击的理解

Journal of Cybersecurity and Privacy Pub Date : 2022-12-14 DOI: 10.3390/jcp2040045

Hunter D. Moore, Andrew Stephens, William Scherer

{"title":"An Understanding of the Vulnerability of Datasets to Disparate Membership Inference Attacks","authors":"Hunter D. Moore, Andrew Stephens, William Scherer","doi":"10.3390/jcp2040045","DOIUrl":"https://doi.org/10.3390/jcp2040045","url":null,"abstract":"Recent efforts have shown that training data is not secured through the generalization and abstraction of algorithms. This vulnerability to the training data has been expressed through membership inference attacks that seek to discover the use of specific records within the training dataset of a model. Additionally, disparate membership inference attacks have been shown to achieve better accuracy compared with their macro attack counterparts. These disparate membership inference attacks use a pragmatic approach to attack individual, more vulnerable sub-sets of the data, such as underrepresented classes. While previous work in this field has explored model vulnerability to these attacks, this effort explores the vulnerability of datasets themselves to disparate membership inference attacks. This is accomplished through the development of a vulnerability-classification model that classifies datasets as vulnerable or secure to these attacks. To develop this model, a vulnerability-classification dataset is developed from over 100 datasets—including frequently cited datasets within the field. These datasets are described using a feature set of over 100 features and assigned labels developed from a combination of various modeling and attack strategies. By averaging the attack accuracy over 13 different modeling and attack strategies, the authors explore the vulnerabilities of the datasets themselves as opposed to a particular modeling or attack effort. The in-class observational distance, width ratio, and the proportion of discrete features are found to dominate the attributes defining dataset vulnerability to disparate membership inference attacks. These features are explored in deeper detail and used to develop exploratory methods for hardening these class-based sub-datasets against attacks showing preliminary mitigation success with combinations of feature reduction and class-balancing strategies.","PeriodicalId":316237,"journal":{"name":"Journal of Cybersecurity and Privacy","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124039206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentially Private Block Coordinate Descent for Linear Regression on Vertically Partitioned Data 垂直分割数据线性回归的差分私有块坐标下降

Journal of Cybersecurity and Privacy Pub Date : 2022-11-09 DOI: 10.3390/jcp2040044

Jins de Jong, B. Kamphorst, S. Kroes

引用次数: 0

Cybersecurity in Hospitals: An Evaluation Model 医院网络安全:一个评估模型

Journal of Cybersecurity and Privacy Pub Date : 2022-10-26 DOI: 10.3390/jcp2040043

M. A. Ahmed, H. Sindi, M. Nour

引用次数: 2