Blockchain Papers

Xingwei Lin, Mingxuan Zhou, Sicong Cao, Jiashui Wang · 5 authors

No abstract is available for this record.

Yi-Shiuan Shen, Kunhua Li, Lin Mao, Wenkai Li · 5 authors

No abstract is available for this record.

Yanli Wang, Sifei Sheng, Yanlin Wang

No abstract is available for this record.

Yuheng Zhang, Liu Pin, Guojun Wang, Peiqiang Li · 8 authors

With the evolution of blockchain technology, the issue of transaction security, particularly on platforms like Ethereum, has become increasingly critical. Front-running attacks, a unique form of security threat, pose significant challenges to the integrity of blockchain transactions. In these attack scenarios, malicious actors monitor other users' transaction activities, then strategically submit their own transactions with higher fees. This ensures their transactions are executed before the monitored transactions are included in the block. The primary objective of this paper is to delve into a comprehensive classification of transactions associated with front-running attacks, which aims to equip developers with specific strategies to counter each type of attack. To achieve this, we introduce a novel detection method named FRAD (Front-Running Attacks Detection on Ethereum using Ternary Classification Model). This method is specifically tailored for transactions within decentralized applications (DApps) on Ethereum, enabling accurate classification of front-running attacks involving transaction displacement, insertion, and suppression. Our experimental validation reveals that the Multilayer Perceptron (MLP) classifier offers the best performance in detecting front-running attacks, achieving an impressive accuracy rate of 84.59% and F1-score of 84.60%.

Leandro Cunha, Miguel A. Brito, Domingos F. Oliveira, Ana Paula Martins

The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.

Bofeng Pan, Natalia Stakhanova, Zhongwen Zhu

Advances in blockchain technology have attracted significant attention across the world. The practical blockchain applications emerging in various domains, ranging from finance, healthcare, and entertainment, have quickly become attractive targets for adversaries. The novelty of the technology coupled with the high degree of anonymity it provides made malicious activities even less visible in the blockchain environment. This made their robust detection challenging. This article presents EtherShield, a novel approach for identifying malicious activity on the Ethereum blockchain. By combining temporal transaction information and contract code characteristics, EtherShield can detect various types of threats and provide insight into the behavior of contracts. The time-interval-based analysis used by EtherShield enables expedited detection, achieving comparable accuracy to other approaches with significantly less data. Our validation analysis, which involved over 15,000 Ethereum accounts, demonstrated that EtherShield can significantly expedite the detection of malicious activity while maintaining high accuracy levels (86.52% accuracy with 1 hour of transaction history data and 91.33% accuracy with 1 year of transaction history data).

Sepideh HajiHosseinKhani, Arash Habibi Lashkari, Ali Mizani Oskui

Smart Contracts (SCs) are crucial in maintaining trust within blockchain networks. However, existing methods for analyzing SC vulnerabilities often lack accuracy and effectiveness, while approaches based on Deep Neural Networks (DNN) struggle with detecting complex vulnerabilities due to limited data availability. This paper proposes a novel approach to analyze Smart Contracts (SCs) vulnerabilities. Our method leverages an advanced form of Genetic Algorithm (GA) and includes the development of a comprehensive benchmark dataset consisting of 36,670 Solidity source code samples. The primary objective of our study is to profile vulnerable SCs effectively. To achieve this goal, we have devised an analyzer called SCsVulLyzer based on Genetic Algorithms, designed explicitly for profiling SCs. Additionally, we have carefully curated a new dataset encompassing a wide range of examples, ensuring the practical validation of our approach. Furthermore, we have established three distinct taxonomies that cover SCs, profiling techniques, and feature extraction. These taxonomies provide a systematic classification and analysis of information, improving the efficiency of our approach. Our methodology underwent rigorous testing through experimentation, and the results demonstrated the superior capabilities of our model in detecting vulnerabilities. Compared to traditional and DNN-based approaches, our approach achieved higher precision, recall, and F1-score, widely used metrics for evaluating model performance. Across all these metrics, our model showcased exceptional results. The customization and adaptations we implemented within the Genetic Algorithm significantly enhanced its effectiveness. Our approach detects smart contract vulnerabilities more efficiently and facilitates robust exploration. These promising results highlight the potential of GA-based profiling to improve the detection of smart contract vulnerabilities, contributing to enhanced security in blockchain networks.

Franciscu SY, Ruggahakotuwa RK, Samarawickrama SWYS, Lahiru JAD

In the rapidly evolving landscape of decentralized systems, ensuring the integrity and trustworthiness of smart contracts is paramount for developers. This paper presents a comprehensive strategy for enhancing smart contract security by focusing on specific high-risk areas, including Integer Overflow, Dangerous Delegate Calls, Timestamp Dependency, Reentrancy Vulnerabilities, Race Conditions, and Sybil attacks. Despite the growing significance of smart contracts in blockchain ecosystems, a notable research gap exists in the development of specialized tools capable of providing real-time vulnerability detection and mitigation guidance. To bridge this gap, our research introduces the ‘GRIFFIN’ - Smart Contracts. Vulnerability Detector is a powerful tool that has been rigorously tested and validated. Our study has yielded significant results, demonstrating the efficacy of the GRIFFIN in proactively identifying and mitigating critical vulnerabilities within a diverse dataset of 12,000 real-world solidity smart contracts. The tool leverages state-of-the-art static analysis techniques and machine learning algorithms, achieving superior accuracy rates when compared to existing solutions. This heightened accuracy not only empowers developers but also boosts the overall robustness and dependability of smart contract ecosystems. The cornerstone of our research is the development and validation of a practical, user-centric solution. By providing actionable insights, code snippets, and real-time feedback to developers, GRIFFIN equips them with the knowledge and tools needed to address vulnerabilities swiftly and effectively. This innovative approach is not merely an academic endeavor but a significant stride towards cultivating resilient and dependable smart contract environments. It instills a culture of security-conscious development practices, ensuring that the smart contracts crucial to decentralized systems can operate with the highest level of trust and reliability. Index Terms— Smart Contracts; Integer overflow; DangerousDelegate call; Timestamp Dependence; Reentrancy Attack; RaceCondition; Sybil Attack; Static Analysis; Detection

Xueyan Tang, Yuying Du, Alan Lai, Ze Zhang · 5 authors

This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process. In this paper, we introduce a solution called Lightning Cat which is based on deep learning techniques. We train three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. Experimental results show that, in the Lightning Cat we propose, Optimized-CodeBERT model surpasses other methods, achieving an f1-score of 93.53%. To precisely extract vulnerability features, we acquire segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately. To demonstrate the feasibility of our proposed solution, we evaluate its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types.

Bowen He, Yuan Chen, Zhuo Chen, Xiaohui Hu · 9 authors

The prosperity of Ethereum attracts many users to send transactions and trade crypto assets. However, this has also given rise to a new form of transaction-based phishing scam, named TxPhish. Specifically, tempted by high profits, users are tricked into visiting fake websites and signing transactions that enable scammers to steal their crypto assets. The past year has witnessed 11 large-scale TxPhish incidents causing a total loss of more than 70 million.

J J Lohith, Kunwar Singh, Bharatesh Chakravarthi

No abstract is available for this record.

Na Cheng, Yin Liang, Shike Li, Jian‐Bin Li

The rapid development of blockchain technology and the rise of Ethereum as its representative platform has triggered a wide range of research and applications. However, this development is also accompanied by new security challenges, among which the Eclipse attack is one of the significant security threats currently facing Ethereum networks. In response to these challenges, we propose an Ethereum Eclipse attack detection method based on a multi-head attention mechanism with Bi-LSTM. This approach utilizes the Bi-LSTM model and multi-head attention mechanism to process time-series data, capturing and focusing on the features most relevant to the Eclipse attack for accurate identification. Additionally, we employ PCA and UMAP dimensionality reduction techniques in data preprocessing to enhance processing efficiency. Experimental results demonstrate that this method distinguishes regular traffic from attack traffic more accurately. Compared to the existing random forest method, our detection approach based on a multi-head attention mechanism with Bi-LSTM achieves a higher detection rate and lower false alarm rate, highlighting its effectiveness in addressing Ethereum network security.

Guillermo Calderón

The verification and validation of academic certifi-cates have become significant challenges in the digital age. With the rise of online education and the proliferation of educational institutions, it is crucial to establish reliable mechanisms to ensure the authenticity of degrees and prevent fraud. In this context, blockchain technology has emerged as a promising solution, offering security, transparency, and trust in academic degree verification. This paper focuses on the role of blockchain technol-ogy and non-fungible tokens (NFTs) as secure and reliable tools for academic degree verification. Blockchain, renowned for its ability to create immutable and transparent records, provides a robust foundation for ensuring the integrity of academic degrees. Additionally, NFTs, which are unique and indivisible tokens, enable the digital representation of degrees and the allocation of exclusive ownership rights to legitimate owners. In this paper, we analyze how the combination of blockchain and NFTs can address current challenges in the validation of academic degrees. We review the different blockchain technologies that enable this task and propose an architecture for implementing such solutions at a reasonable cost. Finally, we examine potential challenges and implementation considerations to ensure the effective adoption of this technology in the educational domain.

Et al. Laxmi Poonia

Computer networks and internet services are increasingly threatened by attacks like Distributed Denial-of-Service (DDoS). DDoS attack mitigation techniques now in use are ineffective due to a lack of resources and a lack of adaptability. Using blockchains like Ethereum, DDoS attacks can be thwarted in innovative ways. With smart contracts, it is possible to track down the IP addresses of attackers without additional hardware. This study examines blockchain-based solutions to combat DDoS attacks for feasibility, effectiveness, as well as cost and performance. The cost model delves into economic aspects like gas, gas price, and Ether value. In it, the evaluation of various smart contracts for the signalization of DDoS attacks is documented and compared to assess three system variants, analyzing gas costs, deployment, speed, and accuracy. It also details Ethereum's ecosystem and how that affects smart contract design and it also acknowledges scalability challenges and suggests outsourcing data for a more scalable solution, advocating for specialized blockchains for DDoS signaling applications. The analysis provides insights into the gas costs associated with different variants, considering various scenarios and highlighting the trade-offs and efficiencies of each approach.

Akshay Kumar, Geetanjali Rathee, Chaker Abdelaziz Kerrache, Muhammad Bilal · 5 authors

Consumer electronics devices, such as refrigerators, washing machines, TVs, smartphones, and household appliances, have become integral to human activities. However, these devices are vulnerable to security breaches and cyber-criminal threats, which can result in the theft and misuse of sensitive information. Existing security surveys and proposed schemes have encountered limitations in terms of redundancy and effectiveness. In this paper, we present a novel approach that ensures secure and transparent communication in consumer electronics. We introduce a multi-criterion decision-making model called TOPSIS, along with a weighted product model, to enhance the security and accuracy of the system. Furthermore, our proposed scheme employs a blockchain system for continuous tracking and monitoring of devices, ensuring accountability and surveillance of their past communications. Through comprehensive validation and verification against existing approaches using various security metrics, our proposed scheme demonstrates superior performance and effectiveness.

B. Sriman, Sushrut Kumar, S. Dhanushram, Kalava Balaji · 5 authors

In this paper, we examine Crypto Jacking which is the significant threat to the world and how this threat is handled by the advanced technologies like Artificial Intelligence. Cryptocurrencies are based on blockchain, a distributed public ledger that keeps track of all transactions that are updated and held by currency holders. Rise of the Cryptocurrency has also led to the rise of Crypto-malware. Crypto jacking is a type of cyber-attack in which cyber criminals use the target's computational power illegally for mining cryptocurrencies. The worldwide danger landscape is becoming dominated by crypto-jacking. The European Union Agency for Cybersecurity's (ENISA) annual study ranked crypto jacking as the third most common cybersecurity threat in 2021. Monero (NMR) is the most popular cryptocurrency for malicious mining since it offers a high level of anonymity for the users and their transactions. Coin Hive is an in-built browser for cryptocurrency mining and this browser allows the malicious software and their programs to mine the Monero (XMR). Though Coin Hive was used legally for the purposes like raising funds it was predominately used for these kinds of illegal mining activities.

Raghu Raman, Vinith Kumar Nair, Prema Nedungadi, Indrakshi Ray · 5 authors

The Darkweb, part of the deep web, can be accessed only through specialized computer software and used for illegal activities such as cybercrime, drug trafficking, and exploitation. Technological advancements like Tor, bitcoin, and cryptocurrencies allow criminals to carry out these activities anonymously, leading to increased use of the Darkweb. At the same time, computers have become an integral part of our daily lives, shaping our behavior, and influencing how we interact with each other and the world. This work carries out the bibliometric study on the research conducted on Darkweb over the last decade. The findings illustrate that most research on Darkweb can be clustered into four areas based on keyword co-occurrence analysis: (i) network security, malware, and cyber-attacks, (ii) cybercrime, data privacy, and cryptography, (iii) machine learning, social media, and artificial intelligence, and (iv) drug trafficking, cryptomarket. National Science Foundation from the United States is the top funder. Darkweb activities interfere with the Sustainable Development Goals (SDG) laid forth by the United Nations to promote peace and sustainability for current and future generations. SDG 16 (Peace, Justice, and Strong Institutions) has the highest number of publications and citations but has an inverse relationship with Darkweb, as the latter undermines the former. This study highlights the need for further research in bitcoin, blockchain, IoT, NLP, cryptocurrencies, phishing and cybercrime, botnets and malware, digital forensics, and electronic crime countermeasures about the Darkweb. The study further elucidates the multi-dimensional nature of the Darkweb, emphasizing the intricate relationship between technology, psychology, and geopolitics. This comprehensive understanding serves as a cornerstone for evolving effective countermeasures and calls for an interdisciplinary research approach. The study also delves into the psychological motivations driving individuals towards illegal activities on the Darkweb, highlighting the urgency for targeted interventions to promote pro-social online behavior.

Borase Bhushan Gulabrao, Digvijaysinh Rathod, Aishwarya Tiwari

No abstract is available for this record.

Niranjan Kala

No abstract is available for this record.

Henri T. Heinonen, Alexander Semenov

Volunteer computing became popular during the late 1990s with software like SETI@home. In the early days of bitcoin mining, CPUs and GPUs were used similarly to volunteer computing: spare computing cycles are used to solve a problem (securing a blockchain in the case of cryptocurrency mining). Then came the specialized hardware of FPGAs and ASICs, and using CPUs and GPUs for bitcoin mining was not profitable anymore. We argue that bitcoin mining has revolutionized the development of specialized hardware and could continue doing so in the industry of unconventional computing. There is also a significant source of unused spare computing cycles in home computers and smartphones that could be used for grid computing purposes with the help of cryptocurrency mining. The notable result from our present analysis is the connection between cryptocurrency mining, grid/volunteer computing, and unconventional computing.

Gang Tian, Xiaojin Wang, Rui Wang, Qiuyue Yu · 5 authors

The classification of the smart contract can effectively reduce the search space and improve retrieval efficiency. The existing classification methods are based on natural language processing technologies. Because the processing of source code by these technologies lacks extraction and processing in the software engineering field, there is still a lot of room for improvement in their methods of feature extraction. Therefore, this paper proposes a multi-feature fusion method for smart contract classification (MFF-SC) based on the code processing technology. From the source code perspective, source code processing method and attention mechanism are used to extract local code features. Structure-based traversal method are used to extract global code features from abstract syntax tree. Local and global code features introduce attention mechanism to generate code semantic features. From the perspective of account transaction, the feature of account transaction is extracted by using TransR. Next, the code semantic features and account transaction features generate smart contract semantic features by an attention mechanism. Finally, the smart contract semantic features are fed into a stacked denoising autoencoder and a softmax classifier for classification. Experimental results on a real dataset show that MFF-SC achieves an accuracy rate of 83.9%, compared with other baselines and variants.

Sukrutha L. T. Vangipuram, Saraju P. Mohanty, Elias Kougianos

No abstract is available for this record.

Gang Wang, Mark Nixon

The proliferation of Internet of Things (IoT) technology has significantly contributed to the realization of ubiquitous computing, facilitating widespread Internet connectivity across diverse application domains and thereby bridging billions of devices and individuals for seamless information collection, data processing, and decision-making. Over the past years, IoT technology has witnessed exponential growth in its applications, particularly in various industrial sectors. Despite these advancements, many existing industrial IoT (IIoT) implementations still rely on centralized architectures, which introduce vulnerabilities to single-point-of-failure attacks and necessitate substantial computational resources at the central entity. To address these challenges and enhance the efficiency and security of IIoT platforms, emerging blockchain technology offers promising prospects. As a distributed and decentralized tamper-resistant ledger, blockchain enables data consistency across multiple locations, making it a compelling solution to tackle issues prevailing in traditional IIoT networks, including heterogeneity, interoperability, and security concerns. The integration of blockchain technology into IIoT platforms, however, presents several critical challenges, inherent to both IIoT and blockchain paradigms, such as standardization, scalability, and interoperability. In this paper, we conduct a comprehensive review of recent advances in architectural design and technology development that aim to address these challenges. We approach this review from a practical perspective by analyzing existing solutions and avoiding potential pitfalls in design. Moreover, we present a selection of representative industrial use cases that stand to benefit from the incorporation of blockchain technology. Finally, we delve into the current research trends and open issues in the domain of blockchain-enabled IIoT platforms, providing insights into future directions for research and development in this burgeoning field.

Mahtab Norouzi, Mounir Elgharabawy, Kaiwen Zhang

Smart contracts are computer programs that run on blockchain networks, enabling secure, transparent, and decentralized transactions. However, the security of smart contracts has always been a critical issue in the blockchain community. One major concern in recent years is the proliferation of honeypots - malicious contracts that deceive users into depositing funds, only to discover that they cannot withdraw their money and have lost their original deposit. In this research, we present a novel classification of honeypots and introduce a new type of contract that allows for the development of a future-proof method for detecting honeypots based on the contract owner and cash flow. We implement this method in a detection tool called HoneyVader, which uses symbolic execution to identify real-world honeypot contracts. Our tool analyzes over 2 million contracts deployed on the Ethereum network, detecting 139 honeypots. By using HoneyVader, we are able to uncover previously unknown zero-day honeypots and new techniques used by attackers, in addition to the ones identified in previous works.