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Lightweight Authentication System Based on Zero Knowledge Proof for IoT Devices 2

Lightweight Authentication System Based on Zero Knowledge Pr...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Khandait, Ashish Umesh Pattanshetti, Tanuja R. Coep Technological University Pune Dept. of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350388916

In the rapidly expanding field of the Internet of Things, ensuring secure and efficient communication between IoT devices and servers is crucial. This paper henceforth designs a lightweight authentication system under the Zero Knowledge Proof (ZKP) protocol, utilizing the HMAC hashing algorithm for the improvement of security measures put in place. This system is designed to enable the authentication of IoT devices without sending their actual password, thus avoiding risks due to password interception at the point of authentication. For instance, if using the phrase "God is great"as the password, one can get it hashed into HMAC with this method and still be assured that it is secure even if proof is intercepted. Simulations in the simulator shall evaluate the system's effectiveness and performance. Performance metrics include accuracy, response time, memory consumption, latency, and throughput. The presented results intend to serve lightweight device authentication mechanisms for IoT devices such that the trustworthiness of IoT devices is achieved without hampering their performance. The presented results are also shown to outperform conventional methods. © 2024 IEEE.

关键词： Authentication Protocol

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

引用

2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Bui, Thanh Duc Panda, Brajendra University of Arkansas Dept. of Computer Science and Computer Engineering Fayetteville United States

ISBN: (纸本)9798350327595

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset. © 2023 IEEE.

关键词： Cloud Computing Cyber Security Data Mining Machine Learning

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Semi-automatic performance support robot that can attach and detach guitars 13

Semi-automatic performance support robot that can attach and...

引用

13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Tsurumi, Kazuki Marutsuka, Ryo Koutaki, Gou Kumamoto University Dept. of Computer Science and Electrical Engineering Kumamoto860-8555 Japan

ISBN: (纸本)9798350355079

Research has primarily focused on fully automatic instrument-playing robots. However, it is difficult to support the experience of playing musical instruments for people with physical disabilities or those who are inexperienced in playing musical instruments. To address this issue, we studied a "semi-automatic"instrument-playing robot based on human-robot collaboration. Our research specifically focused on the guitar, which are stringed instruments with complex pressed strings. The developed robot enables individuals with physical disabilities and those without musical experience to easily play musical instruments. Additionally, the instruments can be easily removed without causing damage. © 2024 IEEE.

关键词： User centered design

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Polygenic Risk Scores are Associated to Aβ-τ Protein Accumulation in Alzheimer's Disease 3

Polygenic Risk Scores are Associated to Aβ-τ Protein Accum...

引用

3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Treccani, Irene Dolci, Giorgio Brusini, Lorenza Cruciani, Federica Galazzo, Ilaria Boscolo Menegaz, Gloria University of Verona Dept. of Engineering for Innovation Medicine Verona Italy University of Verona Dept. of Computer Science Verona Italy

ISBN: (纸本)9798350378009

Alzheimer's (AD) is a complex and multi-domain neurodegenerative disease characterized by amyloid-β (Aβ) and proteins aggregation holding both genotype and phenotype signatures. Increasing evidence suggests that the accumulation of -τ proteins plays a crucial role in disease onset and progression. In order to shade light on these issues, in this study we exploited the Partial Least Squares model to investigate the link between neuroimaging-derived features and genetic factors. Cortical thickness and Polygenic Risk Scores were used as pheno/geno-typing features, respectively. The model was trained on a cohort of subjects including healthy controls (CN) and AD patients, and tested on a discovery set consisting of - τ - and+τ+individuals. The working hypothesis being that these last two groups with different biological profiles would cluster closer to the CN and AD classes, respectively. In agreement with previous results, segregation was observed in the latent space between CN and AD and was also confirmed when applying the model to the - - and++discovery set. These outcomes, together with a significant association found among the respective projection weights, encourage further investigation. © 2024 IEEE.

关键词： Neuroimaging

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Adaptive Forward Error Correction for Voice over IP with Random Linear Network Coding 2

Adaptive Forward Error Correction for Voice over IP with Ran...

引用

2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Su, Yi-Chiuan Wu, Quincy National Chi Nan University Dept. of Computer Science and Information Engineering Nantou Taiwan

ISBN: (纸本)9798331530099

This paper proposes the use of Random Linear Network Coding (RLNC) as an erasure code in the forward error correction (FEC) mechanism for Internet telephony. It also designs and implements a forward error correction algorithm applied to the Real-time Transport Protocol (RTP), which we call Adaptive Systematic RLNC (AS-RLNC). The algorithm learns the current packet loss rate of the network through the Real-time Transport Control Protocol (RTCP) and dynamically adjusts the number of redundant packets sent. Compared with traditional forward error correction methods based on block codes, this approach can enhance the reliability of data transmission and improve audio quality by 31%. © 2024 IEEE.

关键词： Internet telephony

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Strategies for Data Security and Privacy Protection in Cloud Infrastructure 3

Strategies for Data Security and Privacy Protection in Cloud...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Naikwade, Namrata Pathan, Shafi Mit Art Design and Technology University Dept. of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

In today's rapidly growing digital era, cloud computing has emerged as an important technology, providing the ability to scale resources efficiently, accessibility, and cost-effectiveness for storing and processing huge amounts of data. However, alongside its numerous benefits, the adoption of cloud computing has also introduced deep concerns regarding data protection and privacy. The current scenario of cloud computing is characterized by increase in usage of cloud services and an exponential growth of data. While organizations leverage the cloud to enhance agility and innovation, the data sharing ability of cloud and the outsourcing of data management raise significant challenges for ensuring the privacy, integrity, and availability of sensitive data. Moreover, the rising cyber threats, regulatory compliance requirements, and the possibility for unauthorized access bring out the critical importance of robust data protection mechanisms in the cloud *** paper presents a comprehensive survey of strategies for data protection and identity protection in the context of cloud based systems. It examines encryption techniques, access control mechanisms, identity management solutions, among other approaches, highlighting their strengths, limitations, and applicability in different cloud deployment models and use cases. Furthermore, the paper discusses emerging trends such as homomorphic encryption, Attributes Based Encryption (ABE) Schemes and Identity Based Encryption (IBE), which hold promise for enhancing data protection in the developing landscape of cloud technology. © 2024 IEEE.

关键词： Cloud security

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HealthLink: A Cutting-Edge System for Secure Healthcare Data with IoT, Blockchain, and IPFS 3

HealthLink: A Cutting-Edge System for Secure Healthcare Data...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Vijayakumar, Kallur V. Chethan, A.S. Muneshwara, M.S. Swetha, M.S. Anand, R. Shivakumara, T. Bmsit&m Dept. of Mathematics India Bmsit&m Dept. of Computer Science & Engineering India Bmsit&m Dept. of Information Science & Engineering India Bmsit&m Dept. of Mca India

ISBN: (纸本)9798350354348

In today's rapidly evolving healthcare landscape, the integration of cutting-edge technologies such as IoT and wearable devices has led to significant advancements, particularly in the realm of remote patient monitoring. Despite these advancements, the traditional client/server architecture prevalent in current implementations presents substantial challenges related to security and privacy, leaving healthcare systems vulnerable to various attacks. Consequently, stringent regulatory and security measures are imperative to safeguard health data. To address these challenges and meet regulatory requirements, transitioning to a distributed architecture is essential. Blockchain technology, renowned for its distributed nature and robust security features, offers a promising solution to mitigate the security risks inherent in IoT-based systems. Motivated by these considerations, this study introduces HealthLink - a secure healthcare framework that seamlessly integrates IoT and Blockchain *** is designed to facilitate remote patient monitoring, particularly for chronic diseases that require continuous oversight. The framework prioritizes security, scalability, and processing efficiency. Security measures include the use of re-encryption proxies combined with Blockchain for hash data storage, while access control is managed through smart contracts. To enhance Blockchain scalability, an off-chain IPFS-based database is employed for data storage, and the Ethereum Blockchain-based proof of authority is leveraged to expedite data storage processes. As a practical demonstration, we apply the HealthLink system to diabetes management, presenting execution results through system interfaces. Experimental findings highlight substantial enhancements in the security of healthcare systems compared to conventional methodologies, underscoring the potential of HealthLink to revolutionize remote patient monitoring and data security in healthcare. © 2024 IEEE.

关键词： Remote patient monitoring

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Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware 40

Exploring Large Language Models for Semantic Analysis and Ca...

引用

40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Walton, Brandon J Khatun, Mst Eshita Ghawaly, James M Ali-Gombe, Aisha Louisiana State University Dept. of Computer Science and Engineering Baton Rouge United States

ISBN: (纸本)9798331532819

Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering of malware has become increasingly tedious, mainly due to modern malicious codebases' fast evolution and sophistication. Essentially, analysts are tasked with identifying the elusive needle in the haystack within the complexities of zero-day malware, all while under tight time constraints. Thus, in this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-Tiered summarization chain and strategic prompt engineering. Additionally, MalParse performs malware categorization, distinguishing potential malware from benign applications, thereby saving time during the malware reverse engineering process. Despite not being fine-Tuned for Android malware analysis, we demonstrate that through optimized and advanced prompt engineering MalParse can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels. In addition, leveraging the backward tracing of the summaries from package to function levels allowed us to pinpoint the precise code snippets responsible for malicious behavior. © 2024 IEEE.

关键词： Android malware

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Using Blockchain for Incentive-Driven FHIR-based Health Record Exchange 24

Using Blockchain for Incentive-Driven FHIR-based Health Reco...

引用

39th Annual ACM Symposium on Applied Computing, SAC 2024

作者： Rosar, Moritz Chuang, Kun-Ta Dept. of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400702433

Medical data is highly sensitive data, and patients are particularly concerned about the exposure of their identities [1]. A study found that 64% of participants were agreeable to sharing their health data when anonymized, highlighting the importance of privacy in medical data sharing [5]. © 2024 Copyright held by the owner/author(s).

关键词： Sensitive data

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ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platform for Privacy and yet Secured Access 6

ZCube: A Zero-Trust, Zero-Knowledge, and Zero-Memory Platfor...

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6th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Quach, Vinh Dantu, Ram Talapuru, Sirisha Zaman, Shakila Pokharel, Apurba University of North Texas Dept. of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350386745

As concerns over privacy and trust escalate among individuals and corporations, traditional memory-based trust systems are increasingly seen as inadequate and undesirable. The rise of microservice architecture complicates this by making data paths harder to track. Given these concerns, the critical question arises: How can we establish trust or verify an entity's credibility without collecting private information or relying on comparisons with pre-existing data, while knowing the exact data path of each request? To address this challenge, we introduce a lightweight, scalable, ZCube platform that employs nested Zero Knowledge Proofs (ZKPs), eliminating reliance on memory or evaluations based on past actions. This platform verifies and maintains trust in real-time without centralizing trust in any specific part of the system. It anticipates intentions and rigorously monitors them, ensuring operational integrity while preserving privacy. Distinctive for its ZKP trustless setup incorporating blockchain, each request begins anew and is rigorously monitored bidirectionally during execution. Our tests on the realistic example of a microservice-based distributed system, OpenTelemetry Demo, confirm that our novel approach - combining proof segmentation with plan adherence - exhibits fast response times of less than 200ms on average for most test cases. This ZCube platform surpasses traditional methods in efficiency and security. Our analysis shows comprehensive security and enhanced performance while effectively maintaining bidirectional trustlessness. © 2024 IEEE.

关键词： Differential privacy

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