Diana Hawashin, Dunia J. Mahboobeh, Khaled Salah, Raja Jayaraman · 7 authors
Today’s a large number of blood donation management systems fall short in providing traceability, immutability, transparency, audit, privacy, and security features. Also, they are vulnerable to the single point of failure problem due to centralization. In this paper, we propose a private Ethereum blockchain-based solution to automate blood donation management in a manner that is decentralized, transparent, traceable, auditable, private, secure, and trustworthy. The proposed solution stores non-critical and large data off-chain using the decentralized storage of the InterPlanetary File System (IPFS). We present the system architecture, sequence diagrams, entity-relationship diagram, and algorithms to briefly explain the working principles of our blood donation management solution. We evaluate the performance of our solution in terms of efficiency and effectiveness through performing security analysis. We make our smart contract code publicly available on Github1.
In the context of the COVID-19 pandemic, the rapid roll-out of a vaccine and the implementation of a worldwide immunization campaign is critical, but its success will depend on the availability of an operational and transparent distribution chain that can be audited by all relevant stakeholders. In this paper, we discuss how blockchain technology can be used for assuring the transparent tracing of COVID-19 vaccine registration, storage and delivery, and side effects self-reporting. We present such system implementation in which blockchain technology is used for assuring data integrity and immutability in case of beneficiary registration for vaccination, eliminating identity thefts and impersonations. Smart contracts are defined to monitor and track the proper vaccine distribution conditions against the safe handling rules defined by vaccine producers enabling the awareness of all network peers. For vaccine administration, a transparent and tamper-proof side effects self-reporting solution is provided considering person identification and administrated vaccine association. A prototype was implemented using the Ethereum test network, Ropsten, considering the COVID-19 vaccine distribution tracking conditions. The results obtained for each on-chain operation can be checked and validated on the Etherscan, demonstrating various aspects of the proposed system such as immunization actors and safe rules registration, vaccine tracking, and administration. In terms of throughput and scalability, the proposed blockchain system shows promising results.
The first step to realise the true potential of blockchain systems is to explain the associated security risks and vulnerabilities. These risks and vulnerabilities, exploited by the threat agent to affect the valuable assets and services. In this work, we use a security risk management (SRM) domain model and develop a framework to explore two security risks - Sybil and Double-spending - that are observed and considered most concerning security risks within blockchain systems. The framework illustrates the protected assets or assets to secure, the classification of threats that the attacker can trigger using Sybil attack, the identification of threats that cause Double-spending, the vulnerabilities of identified threats, and their countermeasures. We evaluated a newly built framework by exploring Sybil and Double-spending risks in Ethereum-based healthcare applications. We also recognise the various other security and implementation challenges of blockchain that hinder the acceptance of blockchain-enabled solutions. Furthermore, we discuss the permissioned blockchain systems making an appearance in industry-level enterprises and how permissioned blockchain systems control these challenges. We conclude the paper and outline the future work that aims to build an ontology-based blockchain security reference model. The results of this work could help blockchain developers, practitioners, and other associated stakeholders to communicate about Sybil and Double-spending risks, what security countermeasures should be introduced, and what security and implementation challenges are emerging in blockchain systems.
Fredy Andrés Aponte-Novoa, Ana Lucila Sandoval Orozco, Ricardo Villanueva-Polanco, Pedro M. Wightman
The applications that use blockchain are cryptocurrencies, decentralized finance applications, video games and many others. Most of these applications trust that the blockchain will prevent issues like fraud, thanks to the built-in cryptographic mechanisms provided by the data structure and the consensus protocol. However, blockchains suffers from what is called a 51% attack or majority attack, which is considered a high risk for the integrity of these blockchains, where if a miner, or a group of them, has more than half the computing capability of the network, it can rewrite the blockchain. Even though this attack is possible in theory, it is regarded as hard-achievable in practice, due to the assumption that, with enough active members, it is very complicated to have that much computing power; however, this assumption has not been studied with enough detail. In this work, a detailed characterization of the miners in the Bitcoin and Crypto Ethereum blockchains is presented, with the aim of proving the computing distribution assumption and to creating profiles that may allow the detection of anomalous behaviors and prevent 51% attacks. The results of the analysis show that, in the last years, there has been an increasing concentration of hash rate power in a very small set of miners, which generates a real risk for current blockchains. Also, that there is a pattern in mining among the main miners, which makes it possible to identify out-of-normal behavior.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Blockchain technology and, in particular, blockchain-based transaction offers us information that has never been seen before in the financial world. In contrast to fiat currencies, transactions through virtual currencies like Bitcoin are completely public. And these transactions of cryptocurrencies are permanently recorded on Blockchain and are available at any time. Therefore, this allows us to build transaction networks (TN) to analyze illegal phenomenons such as phishing scams in blockchain from a network perspective. In this paper, we propose a Transaction SubGraph Network (TSGN) based classification model to identify phishing accounts in Ethereum. Firstly we extract transaction subgraphs for each address and then expand these subgraphs into corresponding TSGNs based on the different mapping mechanisms. We find that TSGNs can provide more potential information to benefit the identification of phishing accounts. Moreover, Directed-TSGNs, by introducing direction attributes, can retain the transaction flow information that captures the significant topological pattern of phishing scams. By comparing with the TSGN, Directed-TSGN indeed has much lower time complexity, benefiting the graph representation learning. Experimental results demonstrate that, combined with network representation algorithms, the TSGN model can capture more features to enhance the classification algorithm and improve phishing nodes' identification accuracy in the Ethereum networks.
Christof Ferreira Torres, Antonio Ken Iannillo, Arthur Gervais, Radu State
In recent years, Ethereum gained tremendously in popularity, growing from a daily transaction average of 10K in January 2016 to an average of 500K in January 2020. Similarly, smart contracts began to carry more value, making them appealing targets for attackers. As a result, they started to become victims of attacks, costing millions of dollars. In response to these attacks, both academia and industry proposed a plethora of tools to scan smart contracts for vulnerabilities before deploying them on the blockchain. However, most of these tools solely focus on detecting vulnerabilities and not attacks, let alone quantifying or tracing the number of stolen assets. In this paper, we present Horus, a framework that empowers the automated detection and investigation of smart contract attacks based on logic-driven and graph-driven analysis of transactions. Horus provides quick means to quantify and trace the flow of stolen assets across the Ethereum blockchain. We perform a large-scale analysis of all the smart contracts deployed on Ethereum until May 2020. We identified 1,888 attacked smart contracts and 8,095 adversarial transactions in the wild. Our investigation shows that the number of attacks did not necessarily decrease over the past few years, but for some vulnerabilities remained constant. Finally, we also demonstrate the practicality of our framework via an in-depth analysis on the recent Uniswap and Lendf.me attacks.
Christof Ferreira Torres, Ramiro Daniel Camino, Radu State
Ethereum prospered the inception of a plethora of smart contract applications, ranging from gambling games to decentralized finance. However, Ethereum is also considered a highly adversarial environment, where vulnerable smart contracts will eventually be exploited. Recently, Ethereum's pool of pending transaction has become a far more aggressive environment. In the hope of making some profit, attackers continuously monitor the transaction pool and try to frontrun their victims' transactions by either displacing or suppressing them, or strategically inserting their transactions. This paper aims to shed some light into what is known as a dark forest and uncover these predators' actions. We present a methodology to efficiently measure the three types of frontrunning: displacement, insertion, and suppression. We perform a large-scale analysis on more than 11M blocks and identify almost 200K attacks with an accumulated profit of 18.41M USD for the attackers, providing evidence that frontrunning is both, lucrative and a prevalent issue.
Shanqing Yu, Jie Jin, Yunyi Xie, Jie Shen · 5 authors
With the rapid growth of blockchain, an increasing number of users have been attracted and many implementations have been refreshed in different fields. Especially in the cryptocurrency investment field, blockchain technology has shown vigorous vitality. However, along with the rise of online business, numerous fraudulent activities, e.g., money laundering, bribery, phishing, and others, emerge as the main threat to trading security. Due to the openness of Ethereum, researchers can easily access Ethereum transaction records and smart contracts, which brings unprecedented opportunities for Ethereum scams detection and analysis. This paper mainly focuses on the Ponzi scheme, a typical fraud, which has caused large property damage to the users in Ethereum. By verifying Ponzi contracts to maintain Ethereum's sustainable development, we model Ponzi scheme identification and detection as a node classification task. In this paper, we first collect target contracts' transactions to establish transaction networks and propose a detecting model based on graph convolutional network (GCN) to precisely distinguishPonzi contracts. Experiments on different real-world Ethereum datasets demonstrate that our proposed model has promising results compared with general machine learning methods to detect Ponzi schemes.
In the public mind, Bitcoin has often been associated with censorship circumvention and evasion of surveillance measures, specifically in the context of monetary transactions. However, this perceived anonymity is a false sense of security as both on-chain transactions and the underlying message exchange in the peer-to-peer network are attack vectors for deanonymisation and monitoring, as shown in other research. Nonetheless, there has been an increase in Bitcoin usage not only for end-users but also in the context of cybercrime in the form of cryptojacking and ransomware. So there are a number of reasons why proxies might be used in the Bitcoin network, either as a privacy-preserving measure of end-users or as obfuscation in cybercrime.
Kervins Nicolas, Yi Wang, George C. Giakos, Bingyang Wei · 5 authors
Blockchain is a technology that ensures data security by verifying database of records established in a decentralized and distributed network. Blockchain-based approaches have been applied to secure data in the fields of the Internet of Things, software engineering, healthcare systems, financial services, and smart power grids. However, the security of the blockchain system is still a major concern. We took the initiative to present a systematic study which sheds light on what defensive strategies are used to secure the blockchain system effectively. Specifically, we focus on blockchain data security that aims to mitigate the two data consistency attacks: double-spend attack and selfish mining attack. We employed the systematic approach to analyze a total of 40 selected studies using the proposed taxonomy of defensive strategies: monitoring, alert forwarding, alert broadcasting, inform, detection, and conceptual research design. It presents a comparison framework for existing and future research on blockchain security. Finally, some recommendations are proposed for blockchain researchers and developers.
Massimo Bartoletti, James Hsin-yu Chiang, Alberto Lluch Lafuente
Lending pools are decentralized applications which allow mutually untrusted users to lend and borrow crypto-assets. These applications feature complex, highly parametric incentive mechanisms to equilibrate the loan market. This complexity makes the behaviour of lending pools difficult to understand and to predict: indeed, ineffective incentives and attacks could potentially lead to emergent unwanted behaviours. Reasoning about lending pools is made even harder by the lack of executable models of their behaviour: to precisely understand how users interact with lending pools, eventually one has to inspect their implementations, where the incentive mechanisms are intertwined with low-level implementation details. Further, the variety of existing implementations makes it difficult to distill the common aspects of lending pools. We systematize the existing knowledge about lending pools, leveraging a new formal model of interactions with users, which reflects the archetypal features of mainstream implementations. This enables us to prove some general properties of lending pools, such as the correct handling of funds, and to precisely describe vulnerabilities and attacks. We also discuss the role of lending pools in the broader context of decentralized finance.
José Eduardo de Azevedo Sousa, Vinícius Cunha Oliveira, Júlia Almeida Valadares, Alex Borges Vieira · 7 authors
O crescimento do interesse em Ethereum leva a preocupações relacionadas à sua segurança, dado que já houveram ataques que exploraram o seu mecanismo de tarifação ou Penny Attack. Esses ataques afetaram a rede ocasionando lentidão nas transações e há indícios que Ethereum continua susceptível a esse tipo de ataque. Analisamos o comportamento da rede Ethereum durante um Penny Attack, buscando técnicas de aprendizado de máquina para detectá-lo previamente, utilizando atributos das transações. Nossas técnicas tiveram AUC, Fb e recall superior a 94%, 82% e 98% respectivamente.
Ensuring the integrity of executable binaries is of vital importance to systems that run and depend on them. Additionally, supply-chain attacks and security related bugs demonstrate that binaries, once deployed, may need to be revoked and replaced with updated versions.
In the news industry, as well as in social media, fake news detection and identification of news sources has become a central topic of discussion. In the era of digitization, anyone can easily generate or manipulate digital content and publish them on social media websites. On the one hand, these social networking platforms provide ample ease in modern-day communication but on the other hand, using such platforms has posed new challenges to real-world implementation like viral spreading of false/fake information with malicious intentions. In this paper, a naive blockchain and watermarking based social media framework is proposed to control the fake news propagation. We postulate a new blockchain model to mitigate existing challenges in this field. Moreover, the novel solution can help in reducing the spread of fake news by tracing the root or origin of the fake news on social media. Through our experimental results, we show that our blockchain-based solution is able to immediately stream data through a bloXroute server that can propagate data up to 100 times faster than conventional solutions.
Malka N. Halgamuge, Samurdika C. Hettikankanamge, Azeem Mohammad
A sharding mechanism could potentially be the solution to enhance the scalability of blockchain networks and makes the distributed ledger technology more feasible. Despite the scalability improvement, it increases the influence of malicious attacks on blockchain networks. We develop a comprehensive trust model by enhancing the trust score of nodes to minimize the adversary influences of malicious attacks in sharding based blockchain networks. Firstly, a penalty factor is incorporated into this trust model to decrease the probability of malicious nodes becoming leaders in the shards. Then, we examine the leader selection probability for varying penalty factors. We also observe the influence of the global reputation on the trust score for a varying number of nodes. Secondly, we increase the trustworthiness of nodes by including penalty factors and reputation scores to nodes that could then identify the malicious influence. The fair node distribution among shards is achieved by distributing the nodes with the same aggregated trustworthiness scores. Finally, we develop a probability distribution model to identify the probabilities of clustering corrupted nodes into single shards and the existence of such corrupted shards in the entire network. Uncorrupted or honest shard probability is shown to be higher in the RapidChain than the Elastico and OmniLedger sharding protocols. This could be as a result of the shard resiliency of the RapidChain (υ/2) protocol being more significant than that of the Elastico (υ/3) and in OmniLedger (υ/3) protocols. Low message complexity of single intra-shard consensus of the RapidChain protocol O(υ) may contribute to perform security algorithms more efficiently than that of the Elastico O(υ2) and OmniLedger O(υ) sharding protocols. The probabilities of clustering corrupted nodes into single shards can be estimated, and the existence of such corrupted shards in entire networks can be identified using the proposed model.
The production of counterfeit money has a long history. It refers to the creation of imitation currency that is produced without the legal sanction of government. With the growth of the cryptocurrency ecosystem, there is expanding evidence that counterfeit cryptocurrency has also appeared. In this paper, we empirically explore the presence of counterfeit cryptocurrencies on Ethereum and measure their impact. By analyzing over 190K ERC-20 tokens (or cryptocurrencies) on Ethereum, we have identified $2,117$ counterfeit tokens that target 94 of the 100 most popular cryptocurrencies. We perform an end-to-end characterization of the counterfeit token ecosystem, including their popularity, creators and holders, fraudulent behaviors and advertising channels. Through this, we have identified two types of scams related to counterfeit tokens and devised techniques to identify such scams. We observe that over 7,104 victims were deceived in these scams, and the overall financial loss sums to a minimum of \$ 17 million (74,271.7 ETH). Our findings demonstrate the urgency to identify counterfeit cryptocurrencies and mitigate this threat.
Lei Wu, Yufeng Hu, Yajin Zhou, Haoyu Wang · 8 authors
One reason for the popularity of Bitcoin is due to its anonymity. Although several heuristics have been used to break the anonymity, new approaches are proposed to enhance its anonymity at the same time. One of them is the mixing service. Unfortunately, mixing services have been abused to facilitate criminal activities, e.g., money laundering. As such, there is an urgent need to systematically understand Bitcoin mixing services. In this paper, we take the first step to understand state-of-the-art Bitcoin mixing services. Specifically, we propose a generic abstraction model for mixing services and observe that there are two mixing mechanisms in the wild, i.e. {swapping} and {obfuscating}. Based on this model, we conduct a transaction-based analysis and successfully reveal the mixing mechanisms of four representative services. Besides, we propose a method to identify mixing transactions that leverage the obfuscating mechanism. The proposed approach is able to identify over $92$\% of the mixing transactions. Based on identified transactions, we then estimate the profit of mixing services and provide a case study of tracing the money flow of stolen Bitcoins.
Inspired from the iForest algorithmic scheme, we propose an iForest-based blockchain social media anomaly behavior detection method via the improved tree algorithm, for the purpose of isolating the anomalous behaviors as an outlier. The model is integrated with the smart contract structure of blockchain. In the overall system, the user data is sent to the intelligent contract for a period of time. After the identification of the abnormal behavior of social media users, the abnormal behavior in blockchain is marked and stored in the abnormal chain. To a certain extent, the scheme protects users' privacy, improves the efficiency and accuracy of iForest anomaly detection, and is more suitable for multi-dimensional heterogenous data-centric social media user behavior detection.
Smart contract-enabled blockchains allow building decentralized applications in which mutually-distrusted parties can work together. Recently, oracle services emerged to provide these applications with real-world data feeds. Unfortunately, these capabilities have been used for malicious purposes under what is called criminal smart contracts. A few works explored this dark side and showed a variety of such attacks. However, none of them considered collaborative attacks against targets that reside outside the blockchain ecosystem. In this paper, we bridge this gap and introduce a smart contract-based framework that allows a sponsor to orchestrate a collaborative attack among (pseudo)anonymous attackers and reward them for that. While all previous works required a technique to quantify an attacker's individual contribution, which could be infeasible with respect to real-world targets, our framework avoids that. This is done by developing a novel scheme for trustless collaboration through betting. That is, attackers bet on an event (i.e., the attack takes place) and then work on making that event happen (i.e., perform the attack). By taking DDoS as a usecase, we formulate attackers' interaction as a game, and formally prove that these attackers will collaborate in proportion to the amount of their bets in the game's unique equilibrium. We also model our framework and its reward function as an incentive mechanism and prove that it is a strategy proof and budget-balanced one. Finally, we conduct numerical simulations to demonstrate the equilibrium behavior of our framework.
Prashansa Agrawal, Parwat Singh Anjana, Sathya Peri
The surge in the spread of misleading information, lies, propaganda, and false facts, frequently known as fake news, raised questions concerning social media's influence in today's fast-moving democratic society. The widespread and rapid dissemination of fake news cost us in many ways. For example, individual or societal costs by hampering elections integrity, significant economic losses by impacting stock markets, or increases the risk to national security. It is challenging to overcome the spreading of fake news problems in traditional centralized systems. However, Blockchain-- a distributed decentralized technology that ensures data provenance, authenticity, and traceability by providing a transparent, immutable, and verifiable transaction records can help in detecting and contending fake news. This paper proposes a novel hybrid model DeHiDe: Deep Learning-based Hybrid Model to Detect Fake News using Blockchain. The DeHiDe is a blockchain-based framework for legitimate news sharing by filtering out the fake news. It combines the benefit of blockchain with an intelligent deep learning model to reinforce robustness and accuracy in combating fake news's hurdle. It also compares the proposed method to existing state-of-the-art methods. The DeHiDe is expected to outperform state-of-the-art approaches in terms of services, features, and performance.
It is widely accepted that Ethereum mining is highly centralized.\nNonetheless, centralization has been mostly characterized by exclusively\nlooking at the influence that independent miners or mining pools can have over\nthe network. Moreover, models of mining behavior assume that miners are either\nunrelated or only relate via mining pools under highly structured and\ntransparent agreements. If these assumptions and the predictions they entail\nwere to be completely accurate, there would not be any evidence of on-chain\ntransactions between miners, other than the ones expected from mining pool\npayouts. By looking at on-chain transactions between miners in the Ethereum\nNetwork we find that aside from the payouts from mining pools to small miners,\nthere are also transactions that define relationships between mining pools,\nindependent miners and between independent miners and mining pools.\nFurthermore, by characterizing the topology of the network of miner\ntransactions, we find the emergence of highly connected clusters that control\nsignificant amounts of hashing power and exhibit relationships in the opposite\ndirection of what theoretical models predict. This more nuanced\ncharacterization of mining centralization can help identify network\nvulnerabilities and inform protocol redesigns.\n
The blockchain is a distributed ledger technology that is growing in importance since inception. Besides cryptocurrencies, it has also crossed its boundary inspiring various organizations, enterprises, or business establishments to adopt this technology benefiting from the most innovative security features. The decentralized and immutable aspects have been the key points that endorse blockchain as one of the most secure technologies at the present time. However, in recent times such features seemed to be faded due to new attacking techniques. One of the biggest challenges remains within the consensus protocol itself, which is an essential component to bring all network participants to an agreed state. Cryptocurrencies adopt suitable consensus protocols based on their mining requirement, and Proof of Work (PoW) is the consensus protocol that is being predominated in major cryptocurrencies. Recent consensus protocol-based attacks, such as the 51% attack, Selfish Mining, Miner Bribe Attack, Zero Confirmation Attack, and One Confirmation Attack have been demonstrated feasible. To overcome these attacks, we propose Proof of Adjourn (PoAj), a novel consensus protocol that provides strong protection regardless of attackers hashing capability. After analyzing the 5 major attacks, and current protection techniques indicating the causes of their failure, we compared the PoAj against the most widely used PoW, showing that PoAj is not only able to mitigate the 5 attacks but also attacks relying on having a large amount of hashing power. In addition, the proposed PoAj showed to be an effective approach to mitigate the processing time issue of large-sized transactions. PoAj is not tailored to any particular attack; therefore, it is effective against malicious powerful players. The proposed approach provides a strong barrier not only to current and known attacks but also to future unknown attacks based on different strategies that rely on controlling the majority of the hashing power.