Follow blockchain research across journals, conferences, and preprint repositories.
1,615 results · page 35 of 68
Gonzalo Faura, Cezary Siewiersky, Irina Tal
No abstract is available for this record.
Dubey Anand, Siddhartha Choubey
Blockchain technology has emerged as a revolutionary distributed ledger system with the potential to transform various industries, including finance, supply chain, healthcare, and more. However, the decentralized nature of blockchain introduces unique challenges in terms of fraud detection and prevention. This abstract provides an overview of the current state of research and technologies related to fraud detection in blockchain technology-based systems. The paper begins by discussing the fundamental characteristics of blockchain, highlighting its immutability, transparency, and decentralization. These characteristics provide a promising foundation for ensuring data integrity and security but also pose significant challenges in detecting and mitigating fraudulent activities. Next, the paper explores various types of fraud that can occur in blockchain systems, such as double-spending, Sybil attacks, 51% attacks, smart contract vulnerabilities, and identity theft. Each type of fraud is explained along with its potential impact on the integrity and reliability of blockchain systems. To address these challenges, the paper presents an overview of existing fraud detection techniques in blockchain systems. These techniques encompass a range of approaches, including anomaly detection, machine learning algorithms, consensus mechanisms, cryptographic techniques, and forensic analysis. The strengths and limitations of each technique are discussed to provide a comprehensive understanding of their applicability in different scenarios. Furthermore, the paper highlights emerging trends in fraud detection research within the blockchain domain. These trends include the integration of artificial intelligence and blockchain technology, the use of decentralized and federated machine learning approaches, the development of privacy-preserving fraud detection mechanisms, and the utilization of data analytics and visualization techniques for improved detection and investigation. The paper concludes by emphasizing the importance of continuous research and development in fraud detection for blockchain technology-based systems. As blockchain adoption expands across industries, it is crucial to enhance the security and trustworthiness of these systems by effectively detecting and preventing fraud. Future directions for research and potential challenges are also discussed, encouraging further exploration in this vital area of study.
Tao Li, Haolong Wang, Yaozheng Fang, Zhaolong Jian · 6 authors
No abstract is available for this record.
Vishvesh Pathak, B. Uma Maheswari, S. Geetha
No abstract is available for this record.
Gulab Sanjay, S. B. Goyal, Prasenjit Chatterjee
Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.
Manpreet Kaur Aiden, Shweta Mayor Sabharwal, Sonia Chhabra, Mustafa Al-Asadi
No abstract is available for this record.
Mariagrazia Sartori, Indra Seher, P. W. C. Prasad
No abstract is available for this record.
Chi Jiang, Yupeng Chen, Manhua Shi, Yin Zhang⋆
No abstract is available for this record.
Z.J Wei, Weining Zheng, Xiaohong Su, Wenxin Tao · 5 authors
No abstract is available for this record.
Oleksandr Tereshchenko, Наталія Комлева
No abstract is available for this record.
Zimu Hu, Wei‐Tek Tsai, Li Zhang
No abstract is available for this record.
Pvheanushaa Patmanathan, Kavitha Arunasalam, Kahyahthri Suppiah, Dhamayanthi Arumugam
The primary objective of this research is to study the effectiveness of blockchain technology in preventing financial cybercrime. In this research, the researcher intends to know the effectiveness of blockchain technology in preventing financial cybercrime, the white-collar crime which is growing drastically globally. The researcher uses the primary method to collect the data. In this study, four different variables that influence financial cybercrime significantly are immutability, smart contract, distributed ledger technology and consensus algorithm. The data was collected from the targeted respondents which are accountants, IT experts and human resources. Statistical Package of the Social Sciences (SPSS) is being utilized to evaluate the relationship between the four variables which able to influence financial cybercrime. A total of 70 survey questionnaires were delivered to the targeted respondents via a convenience sampling strategy. Responses from 70 participants were entered into SPSS one by one to generate descriptive and inferential statistics. Financial cybercrime is positively correlated with immutability, smart contract, distributed ledger technology and consensus algorithms. As a result, the analysis of the collected data for this research rejects the null hypothesis, while supporting the alternative hypothesis. The conclusion that can derive from this research is that users and organizations should be aware of the financial cybercrime risks that take place around them and the importance to have vital tools that are not vulnerable to malicious attacks. This research creates awareness for users and companies on the usage of blockchain to prevent financial cybercrime.
Urvashi Kishnani, Srinidhi Madabhushi, Sanchari Das
Blockchain's influence extends beyond finance, impacting diverse sectors such as real estate, oil and gas, and education. This extensive reach stems from blockchain's intrinsic ability to reliably manage digital transactions and supply chains. Within the oil and gas sector, the merger of blockchain with supply chain management and data handling is a notable trend. The supply chain encompasses several operations: extraction, transportation, trading, and distribution of resources. Unfortunately, the current supply chain structure misses critical features such as transparency, traceability, flexible trading, and secure data storage - all of which blockchain can provide. Nevertheless, it is essential to investigate blockchain's security and privacy in the oil and gas industry. Such scrutiny enables the smooth, secure, and usable execution of transactions. For this purpose, we reviewed 124 peer-reviewed academic publications, conducting an in-depth analysis of 21 among them. We classified the articles by their relevance to various phases of the supply chain flow: upstream, midstream, downstream, and data management. Despite blockchain's potential to address existing security and privacy voids in the supply chain, there is a significant lack of practical implementation of blockchain integration in oil and gas operations. This deficiency substantially challenges the transition from conventional methods to a blockchain-centric approach.
Simona Ramos, Joshua Ellul
In this article, we develop an interdisciplinary analysis of MEV which desires to merge the gap that exists between technical and legal research supporting policymakers in their regulatory decisions concerning blockchains, DeFi and associated risks. Consequently, this article is intended for both technical and legal audiences, and while we abstain from a detailed legal analysis, we aim to open a policy discussion regarding decentralized governance design at the block building layer as the place where MEV occurs. Maximal Extractable Value or MEV has been one of the major concerns in blockchain designs as it creates a centralizing force which ultimately affects user transactions. In this article, we dive into the technicality behind MEV, where we explain the concept behind the novel Proposal Builder Separation design as an effort by Flashbots to increase decentralization through modularity. We underline potential vulnerability factors under the PBS design, which open space for MEV extracting adversarial strategies by inside participants. We discuss the shift of trust from validators to builders in PoS blockchains such as Ethereum, acknowledging the impact that the later ones may have on users' transactions (in terms of front running) and censorship resistance (in terms of transaction inclusion). We recognize that under PBS, centralized (dominant) entities such as builders could potentially harm users by extracting MEV via front running strategies. Finally, we suggest adequate design and policy measures which could potentially mitigate these negative effects while protecting blockchain users.
Jason Scharfman
No abstract is available for this record.
Rohit Saxena, Deepak Arora, Vishal Nagar, Satyasundara Mahapatra · 5 authors
No abstract is available for this record.
Khaled Gubran Al-Hashedi, Pritheega Magalingam, Nurazean Maarop, Ganthan Narayana Samy · 7 authors
No abstract is available for this record.
Yuan Li, Ran Guo, Guopeng Wang, Lejun Zhang · 9 authors
No abstract is available for this record.
Peng Gong, Wenzhong Yang, Liejun Wang, Fuyuan Wei · 6 authors
Smart contracts have led to more efficient development in finance and healthcare, but vulnerabilities in contracts pose high risks to their future applications. The current vulnerability detection methods for contracts are either based on fixed expert rules, which are inefficient, or rely on simplistic deep learning techniques that do not fully leverage contract semantic information. Therefore, there is ample room for improvement in terms of detection precision. To solve these problems, this paper proposes a vulnerability detector based on deep learning techniques, graph representation, and Transformer, called GRATDet . The method first performs swapping, insertion, and symbolization operations for contract functions, increasing the amount of small sample data. Each line of code is then treated as a basic semantic element, and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph (LG), which shows more structural features that differ from the serialized presentation of the contract. Finally, the node information and edge information of the graph are jointly learned using an improved Transformer–GP model to extract information globally and locally, and the fused features are used for vulnerability detection. The effectiveness of the method in reentrancy vulnerability detection is verified in experiments, where the F1 score reaches 95.16%, exceeding state-of-the-art methods.
Jason Scharfman
No abstract is available for this record.
Anton Krivonogov, Yuriy N. Philippovich, Sergei A. Kesel
No abstract is available for this record.
Matteo Cavallaro, Alban Mathieu
No abstract is available for this record.
Radhika Moondra, Vikas Sihag, Gaurav Choudhary
No abstract is available for this record.
Ankit Mundra, Jai Prakash Mishra, Harshit Jha, Chityanj Sharma
No abstract is available for this record.