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

Counteracting Modeling Attacks Using Hardware-Based Dynamic Physical Unclonable Function 基于硬件的动态物理不可克隆函数对抗建模攻击

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224914

Shailesh Rajput, Jaya Dofe

{"title":"Counteracting Modeling Attacks Using Hardware-Based Dynamic Physical Unclonable Function","authors":"Shailesh Rajput, Jaya Dofe","doi":"10.1109/CSR57506.2023.10224914","DOIUrl":"https://doi.org/10.1109/CSR57506.2023.10224914","url":null,"abstract":"The widespread adoption of Internet of Things (IoT) devices across various application domains has significantly improved quality of life. However, the resource-constrained, heterogeneous, and low-power nature of these devices poses challenges in ensuring secure communication and authenticity. Physical Unclonable Functions (PUFs) provide a solution by creating a unique and device-specific identity through manufacturing process variations without requiring additional resources. To authenticate IoT devices, a challenge-response pair (CRP) is generated based on the unique characteristics of each device. However, the CRPs generated by PUFs often exhibit high correlation, making them vulnerable to modeling attacks. Despite the proposal of numerous intricate PUF architectures, such as XOR PUF and Interpose PDF, the advancement in machine learning algorithms has enabled modeling attacks on these PUFs. This work presents a hardware-based dynamic PUF and evaluates its performance on field programmable gate arrays (FPGAs). The dynamic nature of the proposed PUF architecture makes it challenging for prevalent machine learning models to predict accurate PUF responses. The research also compares the efficacy of logistic regression and multilayer perceptron-based modeling attacks on Arbiter PUF and XOR PUF architectures. The experimental findings reveal that the dynamic PUF outperforms the other two PUFs against machine learning-based attacks. These results suggest that the dynamic PUF architecture is viable for IoT applications.","PeriodicalId":354918,"journal":{"name":"2023 IEEE International Conference on Cyber Security and Resilience (CSR)","volume":"18 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":"116752852","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

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

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

HSchain: Anonymous Permissioned Blockchain with Enhanced Auditability HSchain:具有增强可审计性的匿名许可区块链

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10225006

Jipeng Hou, Lei Xu, Liehuang Zhu, Peng Jiang, Shaorui Song

引用次数: 0

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

Conference Sponsors 会议的赞助商

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/csr57506.2023.10224988

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

VulDetect: A novel technique for detecting software vulnerabilities using Language Models VulDetect:一种使用语言模型检测软件漏洞的新技术

2023 IEEE International Conference on Cyber Security and Resilience (CSR) Pub Date : 2023-07-31 DOI: 10.1109/CSR57506.2023.10224924

Marwan Omar, S. Shiaeles

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