Blockchain Papers

V. E. Sathishkumar, S. Neelakandan, Angela Lee Siew Hoong

No abstract is available for this record.

Emmanuel Chidiebere Eze, Ernest Effah Ameyaw

No abstract is available for this record.

Kadim Lahcen Nadime, Salma Mouatassim, Rajaa Benabbou, Jamal Benhra

No abstract is available for this record.

Ksenia Ganina, Yash Madhwal, Yury Yanovich

No abstract is available for this record.

STEFANUS SAPUTRA

pada level bytecode.Penelitian ini mengimplementasikan metode deteksi malicious smart contract berbasis opcode menggunakan Graph Neural Network (GNN) dengan representasi Control Flow Graph (CFG) pada dataset Forta Network yang terdiri dari 139.600 kontrak dengan rasio ketidakseimbangan kelas 936:1 (149 malicious berbanding 139.451 benign).Setiap smart contract direpresentasikan sebagai CFG di mana basic block menjadi node berfitur 22 dimensi yang terdiri dari frekuensi 14 sensitive opcode dan representasi one-hot tipe instruksi exit, sedangkan hubungan antar blok direpresentasikan sebagai edge dengan lima kategori.Graf yang

Tao Li, Qiong Zhang, Zhengfang Zhang, Yuntian Tan

No abstract is available for this record.

Vishali Aggarwal, Gagandeep

No abstract is available for this record.

Samer Yaghi, Rami Nafee, Ibrahim AbuZaid, Aiman AbuSamra

No abstract is available for this record.

Sabam Parjuangan, Suhardi -, I Gusti Bagus Baskara Nugraha

Small and medium enterprises (SMEs) require secure, efficient, and low-cost digital transaction systems. However, many blockchain-based platforms are designed for large-scale applications and impose significant computational overhead, making them unsuitable for resource-constrained SMEs. This study proposes a lightweight smart contract blockchain platform tailored for SME-scale service environments. The system implements a modular smart contract architecture integrated with a lightweight blockchain and automates key transactional processes, including balance top-ups, service ordering, order confirmation, and payment execution, while ensuring data integrity through a simplified Proof-of-Work mechanism. System performance is evaluated using a Design of Experiment (DOE) framework with a full factorial design and analyzed through Analysis of Variance (ANOVA). The results show that execution time remains below 5 seconds under workloads of up to 20 concurrent transactions, with CPU utilization below 55%. ANOVA results indicate that transaction concurrency and smart contract complexity significantly affect performance, while block size has a limited impact. Security evaluation confirms resistance to unauthorized access, double-spending, and reentrancy attacks.

Mahd Alzoubi

No abstract is available for this record.

Radek SIKUTA

This thesis deals with investing in the cryptocurrency market. The main objective of the thesis is to determine the most suitable investment strategy based on historical data and analysis. The theoretical part is devoted to the introduction of cryptocurrencies, technologies associated with cryptocurencies, legal regulations, and the use of cryptocurrencies as a means of payment. In the practical part, the weak-form efficiency of the cryptocurrency market is first tested using the Wald-Wolfowitz runs test. Subsequently, the investment strategies Buy and Hold, Dollar Cost Averaging (DCA), moving average crossovers, and an equally weighted portfolio are compared. These strategies are evaluated using returns, volatility, Maximum Drawdown, and the Sharpe ratio. For comparison with more traditional markets, external benchmarking with the S&P 500 equity index is conducted.

Davide Sandretto

No abstract is available for this record.

Shihab Sarar, Ali Imran Mehedi, Fabbiha Tahsin Prova, Saha Reno

The modern metropolis essentially demands the use of state‐of‐the‐art, real‐time surveillance systems, which should be reliable, scalable, and respectful of privacy at the same time. Critical shortcomings in traditional architectures are single points of failure, poor scalability, frequent data breaches, and inadequately managed privacy. These aspects of themselves make it inept for the demands of dynamic, fast‐paced city environments, without which reliability, security, and adaptability cannot be compromised at any cost. This brings to light the critical need for innovative and decentralized solutions that can overcome these challenges comprehensively. In our proposed approach, a decentralized framework integrates private blockchain technology via Ethereum, a hybrid cryptography model combining advanced encryption standard (AES) and Rivest–Shamir–Adleman (RSA) encryption, and state‐of‐the‐art deep learning techniques such as YOLOv8, DeepSort, and ArcFace. Blockchain technology ensures metadata is immutable and transparent, thus saving metadata from unauthorized access and tampering. The hybrid cryptography model encrypts sensitive data through AES and securely shares the key of AES through RSA encryption, while decryption is efficiently done in a key management system (KMS). Furthermore, YOLOv8 and DeepSort can be used for high‐precision object detection and real‐time tracking, and ArcFace can be used for facial recognition, meeting the split‐second decision‐making required in urban surveillance. Extensive experiments are performed, and the results indicate that the proposed framework enhances detection precision, tracking accuracy, real‐time responsiveness (60 FPS), and resistance to tampering (>99% chain quality per quorum Byzantine fault tolerance [QBFT]) without compromising efficiency. The adaptive and reliable solution meets modern urban surveillance demands that are evolving at an ever‐increasing pace. The scalability of the operation further ensures enhanced public safety. This paper discusses a decentralized urban surveillance system that is both tamper‐proof and secure using current blockchain technologies, InterPlanetary file system (IPFS), hybrid AES–RSA, and deep learning technologies to mitigate the risks of a traditional centralized system, such as data tampering and privacy violations. The system uses the Ethereum blockchain to provide immutable metadata, the IPFS protocol to create a fully distributed storage system of video and image frames, and an off‐chain KMS service to distribute the keys to the authorized edge devices. The system utilizes real‐time object detection (YOLOv8), tracking (DeepSort), and face recognition (ArcFace) to perform inference locally on the edge devices. We have performed experiments that demonstrate the tamper‐proof and secure scalability with low latency and secure tamper‐proof data integrity of this urban surveillance system in ever‐changing urban environments.

Harsha Gowda R, Sahana M Gowda, Chethan J, Gopika R · 5 authors

The healthcare industry continues to have issues regarding the transparency and trust of the financial transactions, and especially in case of handling of insurance claims and the funding of the patient. Intermediaries and centralization is generally accompanied by inefficiencies, delay and lack of accountability. To eliminate these problems, in this paper, Medicare Chain is proposed as a decentralized blockchain-based fund management system in order to ensure the secure and transparent medical transaction. The system utilizes smart contracts of the Ethereum network to automate the process of transfer of funding between the patients, doctors and donors without the need of centralized authority in the process. Data and transaction logs of nurses is set into the InterPlanetary File System (IPFS) to ensure integrity and prevent any kind of tampering. Django-based web interface allows users authentication, access control and access to the blockchain network. By introducing a framework for auditable, secure and efficient management of medical funds using the concepts of decentralization, the proposed framework shows the possibilities of decentralized systems to create more reliability and trust amongst the healthcare ecosystems.

Rohidas Balu Sangore, Manoj E. Patil

In this paper, we developed anomaly detection based on machine learning-based with the automated signing of the blockchain transaction system to effectively detect the anomalies to prevent the leakage of information from the bitcoin system. Initially, the anomalies data is collected from online resources. The automated signing of the transaction system is performed using machine learning. A blockchain transaction is used for the personalised identification of anomalies transactions. It secures the transactions from fraudulent blockchain transactions. Then, the anomaly detection is done by an optimised recurrent neural network with attention mechanism (ORNN-AM). Here, the parameters are optimised using fitness of firefly and driving training-based optimisation (FFDTO). Anomaly detection with the automated signing of blockchain transactions using machine learning techniques helps to detect anomalies effectively. The performance of anomaly detection with the automated signing of the blockchain transactions system is compared to other conventional anomaly detection models.

Georgios Tsoumas, Pi Lanningham

No abstract is available for this record.

Iida Hallikainen

Tutkimuksen taustalla oli kryptovaluuttojen kasvava merkitys rahoitusmarkkinoilla sekä spot-Bitcoin ETF -rahastojen käyttöönotto Yhdysvalloissa vuonna 2024. Uudet sijoitustuotteet ovat lisänneet yksityissijoittajien mahdollisuuksia saada altistusta Bitcoiniin, mutta samalla ne ovat tuoneet mukanaan uusia riskejä. Tutkimuksen tavoitteena oli tunnistaa spot-Bitcoin ETF -rahastoihin liittyvät keskeiset riskit sekä tarkastella riskienhallinnan keinoja yksityissijoittajan näkökulmasta. Tutkimus toteutettiin integroivana kirjallisuuskatsauksena. Aineisto koottiin Google Scholar- ja ScienceDirect-tietokannoista, ja se rajattiin pääosin vuosien 2024–2025 julkaisuihin. Mukaan valittiin tutkimuksia, jotka käsittelivät spot-Bitcoin ETF- ja ETP-tuotteiden riskejä ja riskimekanismeja. Aineisto analysoitiin vertailemalla tutkimusten keskeisiä havaintoja ja ryhmittelemällä ne laajem-miksi riskiluokiksi. Tulosten perusteella spot-Bitcoin ETF -rahastoihin liittyvät riskit voidaan jäsentää useaan pääluokkaan. Keskeisimpiä olivat volatiliteettiriski, likviditeetti- ja hinnoitteluriski, seuranta- ja rakenneriski, säilytys- ja operatiiviset riskit, sääntely- ja markkinarakenteen riskit sekä käyttäytymisriskit. Tutkimustulokset osoittivat, että ETF-rakenne ei poista Bitcoin-markkinoihin liittyvää voimakasta hinnanvaihtelua, ja että tuotteisiin liittyy myös rakenteellisia ja markkinamekanismeihin liittyviä epävarmuustekijöitä. Tulosten pohjalta muodostettiin yksityissijoittajalle suunnattu riskikehikko, joka kokoaa keskeiset riskit ja auttaa niiden jäsentämisessä. Johtopäätöksenä todettiin, että spot-Bitcoin ETF -rahastot tarjoavat yksityissijoittajalle helpomman ja säännellymmän tavan sijoittaa Bitcoiniin, mutta ne eivät poista sijoittamiseen liittyviä keskeisiä riskejä. Riskienhallinta edellyttää sijoittajalta tuotteen rakenteen ymmärtämistä, kriittistä tiedon arviointia sekä oman riskinsietokyvyn huomioimista. Lisäksi havaittiin, että osa riskeistä liittyy markkinarakenteeseen ja sääntelyyn, eikä niitä voida täysin hallita yksittäisen sijoittajan toimesta.

David Krause

No abstract is available for this record.

Fazal Danish

This OSF project hosts the pre‑registered live forecast for Bitcoin, published as part of Chapter 14 of the book The Luxury Collapse Threshold: How to Predict When Status Symbols Lose Their Power. The forecast was registered before the outcome was known. It includes: a full Luxury Risk Index (LRI) assessment of Bitcoin; an Early Warning Dashboard signal analysis; a predicted trajectory for 2026–2031; explicit confirmation and falsification criteria. This registration is intended to be permanently archived and publicly citable. Readers of the book are invited to verify the forecast and track its accuracy over time.

Matěj Široký

Tato práce se zabývá problematikou šíření transakcí v bitcoinové peer-to-peer síti, jejich monitorováním a měřením doby potřebné na jejich propagaci pro různé kombinace parametrů bitcoinových uzlů a sítě. Cílem této bakalářské práce je pochopit principy a mechanismy používané v bitcoinovém protokolu pro šíření transakcí v síti. Na základě získaných informací je navržen a implementován simulační model, který umožňuje sběr statistických dat o procesu šíření transakcí mezi uzly. Součástí práce je také vytvořený analyzační skript, schopný identifikace uzlů, kterými byly dané transakce vytvořeny.

Jolene Narula

No abstract is available for this record.

Arthur E. Wilmarth

No abstract is available for this record.

Zhaohong Wang

No abstract is available for this record.

Thi Tam Pham, Phuc Hau Nguyen, Рафит Ренатович Набиев

The rapid growth of cloud computing has enabled flexible data storage and sharing; however, it also introduces significant challenges related to security, privacy, and access control. This paper proposes a blockchain-based secure data sharing framework to address the limitations of traditional cloud architectures. The proposed framework integrates distributed ledger technology with smart contracts to enable automated and transparent authentication and access control mechanisms. Data are stored off-chain, while metadata and access permissions are recorded on the blockchain to ensure integrity and traceability. Mathematical models are developed to evaluate the probability of valid access and the effectiveness of the access control mechanism. Analytical results demonstrate that the proposed approach significantly enhances security, mitigates single point of failure risks, and improves resistance against common attacks. Although the use of blockchain introduces additional latency due to consensus mechanisms, the system maintains high scalability. This study provides an effective and practical solution for secure data sharing in distributed cloud environments.