2023 IEEE International Conference on Cyber Security and Resilience (CSR)最新文献_第3页

Moving Target Defense Strategy Selection against Malware in Resource-Constrained Devices 资源受限设备中恶意软件移动目标防御策略选择

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224824

Jan von der Assen, Alberto Huertas Celdrán, Nicolas Huber, Gérôme Bovet, G. Pérez, B. Stiller

{"title":"Moving Target Defense Strategy Selection against Malware in Resource-Constrained Devices","authors":"Jan von der Assen, Alberto Huertas Celdrán, Nicolas Huber, Gérôme Bovet, G. Pérez, B. Stiller","doi":"10.1109/CSR57506.2023.10224824","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224824","url":null,"abstract":"Internet-of-Things (IoT) devices have become critical assets to be protected due to increased adoption for emerging use cases. As such, these devices are confronted with a myriad of malware-based threats. To combat malware in IoT, Moving Target Defense (MTD) is a viable defense layer, since MTD does not rely on a low breach probability - aiming to increase security in a dynamic way. Although evidence supports the usefulness of MTD for IoT, the current state of the art suffers from unrealistic deployments, including the problem of operating multiple MTD techniques. Especially, there is a commonly observed gap in determining and deploying one of a set of locally available MTD techniques. This paper addresses this gap by relying on a rule-based selection mechanism. For that, a risk-driven methodology to establish this selection agent with a well-defined architecture is followed. As an input, the device's behavior, as expressed through its resource consumption, serves as a selection criterion. This architecture was implemented for a Raspberry Pi and evaluated against seven malware, given four existing MTD techniques. The resulting prototype highlights that a rule-based system can efficiently mitigate the malware samples.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"4 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":"123936340","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

DDoS Attack Detection in a Real Urban IoT Environment Using Federated Deep Learning 基于联邦深度学习的真实城市物联网环境中的DDoS攻击检测

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224916

Khatereh Ahmadi, R. Javidan

引用次数: 0

Phishing and Smishing Detection Using Machine Learning 使用机器学习的网络钓鱼和欺骗检测

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224954

Hadi El Karhani, Riad Al Jamal, Yorgo Bou Samra, I. Elhajj, A. Kayssi

引用次数: 0

SPAT: A Testbed for Automotive Cybersecurity Training 汽车网络安全培训的测试平台

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224967

Roberto Caviglia, G. Gaggero, Nicola Vincis, Omar Morando, Alessio Aceti, Mario Marchese

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

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

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

Mitigating Membership Inference Attacks in Machine Learning as a Service 减少机器学习即服务中的成员推理攻击

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224960

Myria Bouhaddi, K. Adi

{"title":"Mitigating Membership Inference Attacks in Machine Learning as a Service","authors":"Myria Bouhaddi, K. Adi","doi":"10.1109/CSR57506.2023.10224960","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224960","url":null,"abstract":"The increasing use of Machine Learning as a Service (MLaaS) has raised privacy and security issues due to membership inference attacks. These attacks can extract sensitive information such as the identification of an individual's participation in a training dataset, by exploiting a binary classifier with limited access. The attacks exploit weaknesses in the decision boundaries of the model, and can lead to the disclosure of private information. However, the current defenses against such attacks, such as those based on differential privacy or regularization, have significant limitations. Therefore, further research is needed to develop effective defenses that maintain the utility of machine learning models while providing formal guarantees, even in the presence of strategic adversaries. In this paper, we focus on mitigating the risks of black-box inference attacks against machine learning models as a service. We propose a defense mechanism that brings the attacker's inference classifier into a zone of uncertainty, rendering it unable to classify a data point as a member or non-member. This mechanism takes into account the attacker's behavior by modeling the interaction between defense and attacker as a game, considering potential gains in confidentiality and costs. Our experiments on two datasets demonstrate the effectiveness of our approach in mitigating membership inference attacks. Furthermore, our defense mechanism outperforms existing defenses by offering superior privacy-utility-performance tradeoffs.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"12 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":"122795734","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

Automated Patch Management: An Empirical Evaluation Study 自动化补丁管理:一个实证评估研究

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224970

Vida Ahmadi Mehri, P. Arlos, E. Casalicchio

引用次数: 0