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19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer security, ESORICS 2024

19th International Workshop on Data Privacy Management, DPM ...

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19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer security, ESORICS 2024

ISBN: (纸本)9783031823480

The proceedings contain 33 papers. The special focus in this conference is on Data Privacy Management. The topics include: reteLLMe: Design Rules for Using Large Language Models to Protect the Privacy of Individuals in Their Textual Contributions;plausible Deniability of Redacted Text;exploring Distribution Learning of Synthetic Data Generators for Manifolds;HEDAS: Secure and Efficient distributed OLAP Using Fully Homomorphic Encryption;card-Based Cryptographic Protocols for Three-Input Functions with a Standard Deck of Cards Using Private Operations;grid-Based Decompositions for Spatial Data Under Local Differential Privacy;balancing Privacy and Utility in Multivariate Time-Series Classification;dynamic k-Anonymity for Electronic Health Records: A Topological Framework;Using Static Code Analysis for GDPR Compliance Checks;privacy-Preserving Tabular Data Generation: Systematic Literature Review;A DPIA Repository for Interdisciplinary Data Protection Research;secrecy and Sensitivity: Privacy-Performance Trade-Offs in Encrypted Traffic Classification;route Discovery in Private Payment Channel networks;A Comparative Study of Rust Smart Contract SDKs for Application-Specific Blockchains;offchain Runtime Verification (for The Tezos Blockchain);quantifying Liveness and Safety of Avalanche’s Snowball;We Will DAG You;assessing the Impact of Sanctions in the Crypto Ecosystem: Effective Measures or Ineffective Deterrents?;Practical Implementation of Pairing-Based zkSNARK in Bitcoin Script;Homomorphic Encryption Based ECDSA Generation Over Five Party Protocol;benchmarking Post-quantum Cryptography in Ethereum-Based Blockchains;exploring Anomaly Detection for Marine Radar systems;hunting Vulnerabilities in the Maritime Domain: A Domain Wide Cybersecurity Vulnerability Analysis;threat Modeling in Satellite Communications for Maritime Operations;cybersecurity Challenges in Industrial Control systems: An Interview Study with Asset Owners in Norway;usage of Cybersecurity Standards

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19th International Workshop on Data Privacy Management, DPM ...

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ISBN: (纸本)9783031823619

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DDoS Attacks: Detection Techniques, Challenges, and Modern Practices

DDoS Attacks: Detection Techniques, Challenges, and Modern P...

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2025 IEEE international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Redekar, Ramesh Bharati, Rajesh Dr. D. Y. Patil Institute of Technology Department of Computer Engineering Pimpri Pune India

ISBN: (纸本)9798331529833

Internet, one of the essential components of today's civilization, serves numerous purposes for individuals, businesses, and society. However, its extensive use has sparked concerns, especially regarding privacy and cybersecurity. Furthermore, cyber dangers are becoming more dangerous, intense, and complicated. distributed Denial of Service (DDoS) attacks have evolved as a prevalent and substantial danger to cybersecurity that may disable the network infrastructures of targeted companies and providers. To guard from DDoS assaults, a many security measures are used, like firewalls and intrusion detection systems. Improving the protective capabilities of IDS frameworks using machine and deep learning, other associated technology, is a popular topic currently. Nevertheless, regardless of considerable improvements, identifying DDoS assaults using machine and deep learning, other associated techs remain a difficulty, particularly when dealing with new DDoS attack. Consequently, this review aims to comprehensively discuss about DDoS attacks by going through the contemporary efforts made in the literature to counter the danger due to the DDoS attacks. First, we investigate certain DDoS attack-related solutions suggested by today's investigators. Finally, we delve deeper by identifying the domains wherein the DDoS attacks are prone to take place;common challenges in recognizing the DDoS attacks in the IoT circumstance;advantages of Software-Defined Networking (SDN);state-of-the-art practices in the academic community to counter DDoS attack attempts in any networks of IoT or SDN or web-connected devices. © 2025 IEEE.

关键词： Cyber attacks

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UARC:Unsupervised Anomalous Traffic Detection with Improved U-Shaped Autoencoder and RetNet Based Multi-clustering 26th

UARC:Unsupervised Anomalous Traffic Detection with Improved ...

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26th international conference on Information and Communications security, ICICS 2024

作者： Xie, Yunyang Chen, Kai Li, Shenghui Li, Bingqian Zhang, Ning Hubei Key Laboratory of Distributed System Security Hubei Engin eering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University Technology Wuhan430074 China

ISBN: (纸本)9789819787975

With the ongoing advancement of deep learning, modern network intrusion detection systems increasingly favor utilizing deep learning networks to improve their ability to learn traffic characteristics. To address the challenge of obtaining a substantial amount of labeled training data, many intrusion detection systems now focus on unsupervised anomaly detection methods. Despite this shift, researchers still face the daunting task of distinguishing a significant volume of anomalous traffic and dealing with data imbalance. To address these real-world challenges, we introduce UARC, a system capable of achieving unsupervised anomaly traffic detection through multi-clustering. UARC utilizes an enhanced U-shaped autoencoder and a feature fusion method incorporating Masked Retnet to effectively extract spatiotemporal features from network traffic. It combines these techniques with the HDBSCAN algorithm for multi-clustering of traffic, providing a form of reverse guidance for network learning. Experimental results on multiple datasets demonstrate that UARC can cluster various types of traffic with an impressive accuracy rate of up to 97.96%, while achieving a 99.70% AUC value for anomaly detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Internet of Things Applicable Authentication and Authorization Based on a Two-Layer Blockchain Approach

Internet of Things Applicable Authentication and Authoriza...

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international conference on Power Engineering and Intelligent systems, PEIS 2024

作者： Kothari, Rakshit Jain, Kalpana Choudhary, Naveen Department of Computer Science and Engineering College of Technology and Engineering Maharana Pratap University of Agriculture and Technology Udaipur India

ISBN: (纸本)9789819767090

The Internet of Things (IoT) connects billions of devices and generates massive amounts of data. However, IoT systems face major security challenges, especially regarding authentication and authorization. Centralized approaches have single points of failure and limited scalability. Blockchain technology offers a decentralized solution, but public blockchains like Bitcoin and Ethereum have limitations for IoT devices due to computational overhead and latency. This paper proposes IoT-optimized two-layer blockchain architecture for authentication and authorization. The first layer is a consortium blockchain optimized for IoT devices. The second layer is a public blockchain for decentralized identity and access control. Cross-chain protocols connect the two layers securely. Linear secret sharing schemes (LSSS) implement fine-grained, attribute-based access control policies in smart contracts. Experiments on Ethereum and Hyperledger Fabric validate the approach’s efficiency and security compared to centralized methods. The two-layer model significantly reduces overhead for IoT devices while retaining decentralization benefits. The architecture provides resilient, scalable access control suitable for distributed IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ethereum

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Application of Hyperledger Blockchain Technology to Logistics Supply Chain with IoT 4

Application of Hyperledger Blockchain Technology to Logistic...

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4th international conference on Evolutionary Computing and Mobile Sustainable networks, ICECMSN 2024

作者： Sayar, Ahmet Kurtbas, Muhammet Cuneyd Sancaktar, Caglayan Kabak, Rana Dudu Cakmak, Sukru Department of Computer Engineering Kocaeli University Kabaoglu Baki Komsuoglu Avenue No:515 Umuttepe Kocaeli İzmit41010 Turkey Atez Software Technologies R&D Center Skyland B Blok No: 4B İstanbul Turkey

Product movement tracking stands as a pivotal aspect of logistics operations. The ever-expanding application of IoT technologies holds significant potential to enhance product tracking in logistics. However, the rapid expansion of IoT usage has also introduced several challenges, particularly concerning data privacy, security, and transparency. Blockchain technology, through distributed ledger systems, offers a secure and transparent means to manage transactions within a network. In this study, the Hyperledger Fabric blockchain network, chosen for its private and permissioned nature, leverages authentication features to ensure the confidentiality, integrity, and security of data from IoT devices. By integrating the widely adopted MQTT protocol, IoT devices involved in logistics processes communicate effectively with the blockchain network. This study presents a system where IoT devices enable the secure and automated tracking of data through blockchain's registration and distribution mechanisms, all of which are demonstrated within a simulation environment. Developed specifically for the logistics sector, this system holds promising potential to address challenges across various fields. © 2025 The Authors. Published by Elsevier B.V.

关键词： distributed ledger

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distributed Denial of Service Attack Detection by Machine Learning Techniques 22

Distributed Denial of Service Attack Detection by Machine Le...

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22nd international Learning and Technology conference, L and T 2025

作者： Jewani, Varkha K. Ajmire, Prafulla E. Atique, Mohd. Sharma, Bhisham Dhaou, Imed Ben Awadhesh Chaurasia, Suhashini Sant Gadge Baba Amravati University Amravati India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset. © 2025 IEEE.

关键词： Network intrusion

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Virtual Collaboration, Organizations, and networks (VCONs) 58

Virtual Collaboration, Organizations, and Networks (VCONs)

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58th Hawaii international conference on System Sciences, HICSS 2025

作者： Nordbäck, Emma Blomqvist, Kirsimarja Lasfer, Assia Hekkala, Riitta Hanken School of Economics Finland LUT Business School Finland Université Laval Canada Aalto University Finland

ISBN: (纸本)9780998133188

This minitrack for HICSS-58 highlights the historical development and continued importance of geographically distributed collaboration and virtual work in organizations and networks. In recent years, the practice has seen significant growth across private, public, and non-profit organizations, reflecting its critical role in today's work environments. The minitrack explores how leaders and employees adapt to, and benefit from new digital forms of collaborating using a wide range of research methods and approaches. The seven papers for this year's VCONSs minitrack illustrate diverse aspects of this evolving practice. © 2025 IEEE computer Society. All rights reserved.

关键词： Adaptation Connectivity Hybrid work Job crafting Leadership Meetings Virtual Collaboration

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Enhancing Healthcare Recommendations for Special-Need Patients Using Deep Learning and Blockchain security 15th

Enhancing Healthcare Recommendations for Special-Need Patien...

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15th international conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Mudhafar, Mustafa Raparthi, Mohan Thota, Vijayakumar Sivakumar, B.N. Degadwala, Sheshang Department of Medical Physics College of Applied Medical Sciences University of Kerbala Karbala56001 Iraq Alphabet Life Science DallasTX75063 United States NSB Business School Bangalore University Bangalore India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Special needs patients deserve increasingly specialised and secure healthcare supervision as the healthcare environment evolves. Our ground-breaking approach combines deep learning and blockchain security to enhance healthcare recommendations for people with special needs. This novel approach might assist with the difficulties of providing tailored healthcare advice. To get the best results, we mix two efficient technologies. Deep learning allows you to detect specific patterns in large patient datasets. Modern neural networks and algorithms are what power it. In the recommended approach, deep learning insights resulted in 92% accuracy, 89% precision, 94% recall, and a 91% F1 score. Deep learning allows for extremely accurate and customised healthcare suggestions. Special needs individuals need customised treatment regimens due to their specific medical challenges. Blockchain security is critical for ensuring patient data validity and confidentiality. To secure patient data, blockchain technology generates a distributed, immutable ledger and an open, auditable database. Encryption protects data's privacy, prohibiting unauthorised access to patient data. These developments in healthcare advice may increase treatment quality and patient trust in the system. Our analysis showed positive and game-changing findings, with the suggested technique outperforming traditional methods. These results illustrate the system's effectiveness and potential to revolutionise special-needs healthcare. This study reveals that the proposed approach is durable and has the potential to significantly alter healthcare recommendations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Deep learning Healthcare Integration Patients Recommendations security Special needs Technology Treatment

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Federated Clustering Defense: Mitigating Data Exposure in Collaborative Learning

Federated Clustering Defense: Mitigating Data Exposure in Co...

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2025 international conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Radhakrishnan, Anuja Krishna, Sathya Bama Sathyabhama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331507541

The development of intelligent industrial applications is being driven globally by advancements in the next generation of Internet of Things (IoT) technologies. At the same time, artificial intelligence (AI) technologies like machine learning and deep learning are becoming more widely used. Conventional machine learning models primarily rely on large data sets. However, the vast data sets generated by network-edge devices are expensive, inefficient to acquire and handle, and they pose major privacy hazards. Federated learning (FL) is a novel paradigm for statistical model training on distributed edge networks that allows data to take part in the training of federated models without being localized. Traditional machine learning issues with low data usage, data privacy, and information security brought on by data isolation can be resolved with this method. We propose a novel Federated Clustering with Differential Privacy (FedCDP) algorithm designed to efficiently model user behavior in an unsupervised manner. This algorithm focuses on optimizing computation and storage requirements while minimizing data leakage through robust differential privacy mechanisms. © 2025 IEEE.

关键词： Deep learning

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