Follow blockchain research across journals, conferences, and preprint repositories.
2,015 results · page 14 of 84
Cui Bo, Jia Zhang
No abstract is available for this record.
Peng Jiang, Liehuang Zhu
No abstract is available for this record.
Haomeng Xie, Zheng Yan, C.‐J. Richard Shi
No abstract is available for this record.
H. Howie Huang, Jie Wang
No abstract is available for this record.
A.C. Kaladevi, Lathika Valavan, Sowndharya Rajendran, Ramya Perumal
No abstract is available for this record.
Pandi Vijayakumar, S. C. Rajkumar, S. Audithan, P. Jayarekha · 5 authors
No abstract is available for this record.
Quan Nguyen, Pham Thi Thanh, Trần Thiện Thanh, Nguyen Huu Quyen · 6 authors
No abstract is available for this record.
Somchart Fugkeaw, Archawit Changtor, Thanabordee Maneerat, Pakapon Rattanasrisuk · 5 authors
The Industrial Internet of Things (IIoT) generates a vast volume of sensitive data that demands not only confidentiality but also authenticity and integrity—especially in large-scale deployments. Ensuring that data originates from trusted devices is critical; however, existing authentication mechanisms often lack scalability and effective revocation support. To address these challenges, we proposeLightPUF-IIoT, a secure and lightweight authentication scheme designed for fog-assisted IIoT data sharing. The scheme leveragesPhysical Unclonable Functions (PUFs)andNon-Interactive Zero-Knowledge Proofs (NIZKPs)to enable scalable, group-based authentication for devices and fog nodes. By binding authenticated identities to cryptographic tokens used during data transmission, LightPUF-IIoT ensures data authenticity and supports real-time rogue device detection. The scheme also includes efficient mechanisms for device revocation and secure token regeneration. Experimental results show that LightPUF-IIoT provides strong security guarantees with minimal resource overhead and significantly outperforms existing approaches in terms of computational cost, scalability, and authentication throughput.
Serena Gomez, Chaitanya Vijaykumar Mahamuni, Angelin Abisha, Aditi Patil
<p><strong>This paper contributes to the literature by</strong> presenting a reproducible framework for designing and testing fair on-chain governance systems. It introduces <em>CryptoKen</em>, an Ethereum-based token using quadratic voting to reduce plutocracy and enhance participation in decentralized organizations, achieving high usability (SUS 82.5) and 92% verified test coverage.</p>
Feng Yu, Ryan Laird
The rise of blockchain and Digital Ledger Technology (DLT) has gained wide traction. Instead of relying on a traditional centralized data authority, a blockchain system consists of digitally entangled block data shared across a distributed network. The specially designed chain data structure and its consensus mechanism protect blockchain data from being tampered by unauthorized adversaries. However, implementing a full-fledged blockchain system to protect a database can be technically cumbersome. In this work, we introduce an in-database design, named chain table, to protect data integrity without the need for a blockchain system. It features a succinct design without significant technology barriers or storage overhead. To realize rigorous data security, we also propose a set of data writing principles for the chain table. We prove that the chain table, together with the data writing principles, will guarantee flexible data integrity, named table-level data integrity (TDI).
Xiaoqi Li, Wenkai Li, Zhiquan Liu, Yuqing Zhang · 5 authors
Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes that may occur during malicious events. However, this victim-only approaches seldom possess the capability to cover the attacker’s interaction intention logic. Furthermore, only a minuscule percentage of DeFi protocols experience attacks in real-world scenarios, which poses a significant challenge for these detection tools to demonstrate practical effectiveness. In this paper, we propose DeFiTail, thefirstframework that utilizes deep learning technology for access control and flash loan exploit detection. Through feeding the cross-contract static data flow, DeFiTail automatically learns the attack logic in real-world malicious events that occur on DeFi protocols, capturing the threat patterns between attacker and victim contracts. Since the DeFi protocol events involve interactions with multi-account transactions, the execution path with external and internal transactions requires to be unified. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, DeFiTail validates the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Comparative experiment results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset. By monitoring existing contracts, we identified five distinct categories of vulnerabilities: repetition abuse, unsafe unintended exploitation, signature violated exploitation, insecure interfaces exploitation, and unrestricted token transfer.
Mariana Durcheva, Malinka Ivanova
No abstract is available for this record.
Varsha Arya, Kwok Tai Chui, Ching‐Hsien Hsu, Brij B. Gupta · 5 authors
No abstract is available for this record.
Zheng Che, Meng Shen, Zhehui Tan, Hanbiao Du · 9 authors
With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious accounts is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious account detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious account detection remains a challenging task. In this paper, we propose ShadowEyes, a framework for detecting malicious accounts by leveraging interaction feature learning with only a small labeled dataset. Specifically, We first propose a generalized account representation named TxGraph, which captures the universal interaction features of Ethereum and Bitcoin. Then we carefully design an account representation augmentation method tailored to simulate the evolution of malicious accounts to generate positive pairs. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the scenario of acrossplatform malicious account detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. In the zero-shot learning scenario, it can achieve an F1 score of 79.56% for detecting gambling accounts, surpassing the SOTA method by 10.44%.
Chris Gilbert, Mercy Abiola Gilbert
The rapid expansion of cryptocurrencies has revolutionized the digital economy, offering decentralized and secure transaction mechanisms through blockchain technology. However, this growth has concurrently attracted cybercriminal activities, exploiting the inherent anonymity and security features of cryptocurrencies for illicit purposes such as money laundering, ransomware, exchange hacking, tax evasion, Initial Coin Offering (ICO) frauds, Ponzi schemes, and phishing attacks. This paper provides a comprehensive analysis of cryptocurrency-related cybercrimes, identifying prevalent patterns and underlying vulnerabilities within blockchain systems and cryptocurrency exchanges. Utilizing a multifaceted research methodology that includes qualitative and quantitative analyses, case studies, and theoretical frameworks like the Martial Arts Matrix (MAM), the study elucidates the motivations and sophisticated tactics employed by cybercriminals. Key findings highlight the critical need for enhanced security measures, robust regulatory frameworks, and collaborative efforts among stakeholders to mitigate these risks effectively. Additionally, the paper explores emerging trends and technologies in blockchain security, such as decentralized identity management and quantum-resistant cryptographic algorithms, which hold promise for strengthening defenses against evolving cyber threats. The study concludes by offering actionable recommendations for law enforcement agencies, cryptocurrency providers, and policymakers to address the dynamic landscape of cryptocurrency-related cybercrimes, ensuring the sustained growth and trustworthiness of the cryptocurrency ecosystem.
Yejin Kwon, Youngbom Park
No abstract is available for this record.
Xinzhuo Zhang, Haoyu Gao, Lei Hong, Tiankai Xu
No abstract is available for this record.
Iuon‐Chang Lin, Yu-Chun Chan
No abstract is available for this record.
Sara Fatima, Mohammad Pasha
No abstract is available for this record.
S. Rajaprakash, C. Bagath Basha, Karthik Karthik, Amjad Ali · 7 authors
No abstract is available for this record.
Chen Zhang, Su Peng
Developers and users are drawn to Ethereum due to its rapidly growing asset count. However, potential vulnerabilities and malicious behaviors during the execution of smart contracts have led to an increasing demand for security detection technology. Conventional static and dynamic analysis methods are less useful in the case of complex opcode sequences and multiple execution paths. To tackle this problem, this paper proposes an Ethereum intrusion detection method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network with multi-head attention. It examines the opcode execution paths generated from the intra-and-inter-function Control Flow Graphs (CFGs) using the EPP algorithm and captures the rich feature representations and long dependencies. This combination increases the precision and efficacy of detecting malicious activity and smart contract vulnerabilities while simultaneously enhancing the model’s robustness and interpretability and handling variable-length sequences. For the five selected vulnerabilities, the precision, recall and F1-score of this model are above 89.9%, 87.3%, and 88%, respectively.
Z. Chen, Jianyong Yu, Yulong Wang
No abstract is available for this record.
Jiaze Sun, Zhiqiang Yin, Hengshan Zhang, Xiang Chen · 5 authors
No abstract is available for this record.
Yuan Qin, Tengfei Ma, Hongzhou Chen, Haihan Duan
Airdrops represent a pivotal strategic instrument for Web3 projects, serving to distribute free tokens and motivate early adoption. However, the popularity of these tokens has fueled the emergence of airdrop hunters—individuals who exploit multiple transactions to acquire disproportionate amounts of tokens unfairly. This phenomenon threatens the integrity and fairness of the Web3 community. Current detection methods struggle with high false-positive rates, harming legitimate users, and require significant computational resources for training. Furthermore, these methods face challenges in adapting to the evolving tactics of airdrop hunters, leading to diminished detection accuracy and efficiency. We introduce ARTEMIX, a community-boosting-based framework that integrates custom-engineered features and community detection techniques to identify airdrop hunters in NFT transactions. Using data from the Blur NFT market, ARTEMIX demonstrates superior accuracy and efficiency, outperforming existing graph-based inference models, achieving an F1 score of 0.898. This approach provides a scalable and effective solution to anomaly detection in the Web3 ecosystem, promoting a more secure and equitable environment for token distributions.