2023 IEEE International Conference on Cyber Security and Resilience (CSR)最新文献_第2页

Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies 介绍TRUMPET项目:可信赖的多站点隐私增强技术

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224961

A. Pedrouzo-Ulloa, J. Ramon, Fernando Péerez-González, Siyanna Lilova, Patrick Duflot, Zakaria Chihani, N. Gentili, P. Ulivi, Mohammad Ashadul Hoque, Twaha Mukammel, Zeev Pritzker, Augustin Lemesle, J. Loureiro-Acuña, Xavier Martínez, G. Jiménez-Balsa

{"title":"Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies","authors":"A. Pedrouzo-Ulloa, J. Ramon, Fernando Péerez-González, Siyanna Lilova, Patrick Duflot, Zakaria Chihani, N. Gentili, P. Ulivi, Mohammad Ashadul Hoque, Twaha Mukammel, Zeev Pritzker, Augustin Lemesle, J. Loureiro-Acuña, Xavier Martínez, G. Jiménez-Balsa","doi":"10.1109/CSR57506.2023.10224961","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224961","url":null,"abstract":"This paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research has cast a shadow of doubt on its strength for privacy protection. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross-border European datasets with privacy guarantees that follow the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients' privacy.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121747149","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

Efficiency of Malware Detection Based on DNS Packet Analysis Over Real Network Traffic 基于DNS报文分析的真实网络流量恶意软件检测效率研究

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224973

Lorenzo Principi, M. Baldi, A. Cucchiarelli, L. Spalazzi

引用次数: 0

Assessing Cybersecurity Hygiene and Cyber Threats Awareness in the Campus - A Case Study of Higher Education Institutions in Portugal and Poland 评估校园网络安全卫生和网络威胁意识——以葡萄牙和波兰高等教育机构为例

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224910

Luís Oliveira, A. Chmielewski, Paulina Rutecka, K. Cicha, Mariia Rizun, Nuno Torres, Pedro Pinto

{"title":"Assessing Cybersecurity Hygiene and Cyber Threats Awareness in the Campus - A Case Study of Higher Education Institutions in Portugal and Poland","authors":"Luís Oliveira, A. Chmielewski, Paulina Rutecka, K. Cicha, Mariia Rizun, Nuno Torres, Pedro Pinto","doi":"10.1109/CSR57506.2023.10224910","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224910","url":null,"abstract":"Cybersecurity skills are of utmost importance to prevent or mitigate the impact of cyberattacks. In higher education, there are graduations related to Information Technology (IT), where students are expected to develop technical skills, including cybersecurity. Thus, it is relevant to assess students' cybersecurity awareness regarding cybersecurity hygiene and cyber threats when they start their academic studies and to verify whether there are context-dependent differences. This paper presents the results of an assessment regarding the cybersecurity awareness level of 110 first-year students from computer science graduations from two different countries, Poland and Portugal. The assessment was designed as a survey divided into the following two main groups of questions: (1) awareness regarding cybersecurity hygiene and (2) awareness regarding major cyber threats considered in the European Union Agency for Cybersecurity (ENISA) 2021 cyber threat report. The survey results show that Polish and Portuguese students present different self-perceptions and knowledge regarding cybersecurity hygiene and knowledge of cybersecurity. In these areas, Polish students are generally more confident than Portuguese students. Also, Polish students presented better scores around 70%, against the ones obtained by the Portuguese students, scoring around 58%.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133038624","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

Federated Learning-Based Credit Card Fraud Detection: Performance Analysis with Sampling Methods and Deep Learning Algorithms 基于联邦学习的信用卡欺诈检测:使用采样方法和深度学习算法的性能分析

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224978

Nahid Ferdous Aurna, Md. Delwar Hossain, Yuzo Taenaka, Y. Kadobayashi

{"title":"Federated Learning-Based Credit Card Fraud Detection: Performance Analysis with Sampling Methods and Deep Learning Algorithms","authors":"Nahid Ferdous Aurna, Md. Delwar Hossain, Yuzo Taenaka, Y. Kadobayashi","doi":"10.1109/CSR57506.2023.10224978","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224978","url":null,"abstract":"The exponential technological advancement is turning everyone towards an easy and efficient way of financial transactions. Consequently, the use of credit cards is rising substantively, creating a more incredible opportunity for fraudsters which is an alarming concern nowadays since a fraudster may use several tools, techniques and tactics to make a fraudulent transaction. As a countermeasure, an effective fraud detection mechanism and highly sensitive data privacy preservation are imperative to detect fraudulent transactions. This paper proposes a Federated Learning (FL)-based fraud detection system since its key feature preserves the privacy of highly sensitive data, wherein the model could be trained without sharing the credit card data in the cloud. We contemplate three Deep Learning (DL) models: Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) regarding the FL approach. Subsequently, to overcome the data imbalance issue, four distinct sampling techniques are explored to inspect the impact on the traditional centralized and FL approaches. Finally, we further investigate and compare FL-based detection systems with diversified state-of-the-art models. Our experimental results demonstrate that the proposed method is superior compared with state-of-the-art methods and achieves high detection rate of 99.51%, 98.77% and 98.20% respectively for CNN, MLP and LSTM models.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122188197","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

Cyber Incident Scenarios in the Maritime Industry: Risk Assessment and Mitigation Strategies 海运业中的网络事件情景:风险评估和缓解策略

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224972

Mohamed Ben Farah, M. Al-Kadri, Yussuf Ahmed, Raouf Abouzariba, Mohamed. Benfarah, Omar. Alkadri, Yussuf Ahmed, X. Bellekens

引用次数: 0

Continuous Security Assurance of Modern Supply-Chain Ecosystems with Application in Autonomous Driving: The FISHY approach for the secure autonomous driving domain 现代供应链生态系统的持续安全保障及其在自动驾驶中的应用:安全自动驾驶领域的FISHY方法

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224971

George Hatzivasilis, S. Ioannidis, Grigoris Kalogiannis, Manolis Chatzimpyrros, G. Spanoudakis, Guillermo Jiménez Prieto, Araceli Rojas Morgan, Miguel Juaniz Lopez, C. Basile, J. F. Ruiz

{"title":"Continuous Security Assurance of Modern Supply-Chain Ecosystems with Application in Autonomous Driving: The FISHY approach for the secure autonomous driving domain","authors":"George Hatzivasilis, S. Ioannidis, Grigoris Kalogiannis, Manolis Chatzimpyrros, G. Spanoudakis, Guillermo Jiménez Prieto, Araceli Rojas Morgan, Miguel Juaniz Lopez, C. Basile, J. F. Ruiz","doi":"10.1109/CSR57506.2023.10224971","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224971","url":null,"abstract":"Cyber security always forms a significant aspect of ICT infrastructure, with threats on supply-chain networks gaining greater attention nowadays. The secure autonomous driving domain presents a unique set of challenges for supply-chain security. Autonomous vehicles rely on a complex ecosystem of hardware and software components, many of which are sourced from third-party suppliers. Ensuring the security and reliability of this supply-chain is essential to maintain the safety and viability of autonomous driving as a technology. To address these challenges, a continuous security assurance approach is necessary. This involves ongoing monitoring, assessment, and improvement of security measures to detect and mitigate potential vulnerabilities in the supply chain. Key measures may include regular vulnerability assessments, penetration testing, and security awareness training for employees and contractors, as well as the implementation of security controls such as secure communication protocols, access controls, and intrusion detection systems. By adopting a continuous security assurance approach for supply chain security in the secure autonomous driving domain, organizations can safeguard their operations and ensure the safety of passengers and other road users. This paper presents a security assurance and certification solution for supply-chain services. Security elements are continuously assessed based on AI operations. The proposal is implemented under the EU funded project FISHY and applied in the supply-chain of secure autonomous driving (SADE) pilot with REMOTIS smart vehicles. Nevertheless, it is a generic solution that can be applied in any domain.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132383108","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

Control-Implications-Based Side-Channel Monitoring for Embedded Systems 基于控制含义的嵌入式系统侧信道监控

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224942

Sandip Roy, Benjamin Drozdenko

引用次数: 0

Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins Design 融合基于模型和数据驱动的弹性系统数字孪生设计方法

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224945

L. Campanile, Maria Stella de Biase, Roberta De Fazio, Michele Di Giovanni, F. Marulli, Laura Verde

引用次数: 0

FedGA-Meta: Federated Learning Framework using Genetic Algorithms and Meta-Learning for Aggregation in Industrial Cyber- Physical Systems FedGA-Meta:在工业网络物理系统中使用遗传算法和元学习进行聚合的联邦学习框架

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224975

Souhila Badra Guendouzi, Samir Ouchani, Hiba El Assaad, Madeleine El Zaher

引用次数: 0

Comparative Analysis of Pattern Mining Algorithms for Event Logs 事件日志模式挖掘算法的比较分析

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224996

Orkhan Gasimov, Risto Vaarandi, Mauno Pihelgas

引用次数: 0